Transactions on Intelligent Welding Manufacturing: Volume V No. 1 2021 9789819996292, 9819996295

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Shanben Chen Yuming Zhang Zhili Feng   Editors

Transactions on Intelligent Welding Manufacturing Volume V No. 1 2021

Transactions on Intelligent Welding Manufacturing Editors-in-Chief Yuming Zhang University of Kentucky USA

Shanben Chen Shanghai Jiao Tong University PRC

Zhili Feng Oak Ridge National Laboratory USA

Honorary Editors G. Cook, USA K. L. Moore, USA Ji-Luan Pan, PRC

S. A. David, USA S. J. Na, KOR Lin Wu, PRC

Y. Hirata, JAP J. Norrish, AUS

T. Lienert, USA T. J. Tarn, USA

X. Q. Chen, NZL D. Hong, USA W. Zhou, SGP

D. Du, PRC X. D. Jiao, PRC

D. Fan, PRC I. Lopez-Juarez, MX

Guest Editors H. P. Chen, USA J. C. Feng, PRC H. J. Li, AUS

Regional Editors Asia: L. X. Zhang, PRC America: Y. K. Liu, USA

Australia: Z. X. PAN, AUS Europe: S. Konovalov, RUS

Associate Editors Q. X. Cao, PRC B. H. Chang, PRC J. Chen, USA H. B. Chen, PRC S. J. Chen, PRC X. Z. Chen, PRC A.-K. Christiansson, SWE Z. Q. Feng, PRC Z. G. Li, PRC Y. Han, PRC X. M. Hua, PRC

Y. Huang, USA S. Konovalov, RUS W. H. Li, PRC X. R. Li, USA Y. K. Liu, USA L. M. Liu, PRC H. Lu, PRC Q. H. Lu, PRC Z. Luo, PRC G. H. Ma, PRC Pedro Neto, PRT

G. Panoutsos, UK Z. X. Pan, AUS X. D. Peng, NL Y. Shi, PRC S. M. Sharma, India A. N. Siddiquee, India J. Wu, USA J. X. Xue, PRC L. J. Yang, PRC X. H. Yang, PRC Z. S. Yu, PRC

M. Wang, PRC S. Wang, PRC X. W. Wang, PRC Z. Z. Wang, PRC G. J. Zhang, PRC H. J. Zhang, PRC H. Zhang, B, PRC H. Zhang, N, PRC L. X. Zhang, PRC W. J. Zhang, USA

H. Y. Wang, PRC S. L. Wang, PRC J. Xiao, PRC J. J. Xu, PRC Y. L. Xu, PRC C. Yu, PRC

D. Wu, PRC M. H. Wu, PRC H. W. Yu, PRC K. Zhang, PRC W. Z. Zhang, PRC Z. F. Zhang, PRC

Academic Assistant Editors J. Cao, PRC B. Chen, PRC Z. Y. Chen, USA Y. S. He, PRC Y. M. Huan, PRC Y. Luo, PRC

N. Lv, PRC F. Li, PRC S. B. Lin, PRC Y. Shao, USA Y. Tao, PRC J. J. Wang, PRC

Editorial Staff Executive Editor (Manuscript and Publication): Responsible Editor (Academic and Technical):

Dr. Yan Zhang, PRC Dr. Jing Wu, USA

Shanben Chen Yuming Zhang Zhili Feng Editors •

•

Transactions on Intelligent Welding Manufacturing Volume V No. 1 2021

123

Editors Shanben Chen Shanghai Jiao Tong University Shanghai, China Zhili Feng Oak Ridge National Laboratory Oak Ridge, TN, USA

Yuming Zhang Department of Electrical and Computer Engineering University of Kentucky Lexington, KY, USA

ISSN 2520-8519 ISSN 2520-8527 (electronic) Transactions on Intelligent Welding Manufacturing ISBN 978-981-99-9628-5 ISBN 978-981-99-9629-2 (eBook) https://doi.org/10.1007/978-981-99-9629-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Editorial

This issue of the Transactions on Intelligent Welding Manufacturing (TIWM) is a collection partially selected from the high-quality contributions recommended by the “2022 International Conference on Robotic Welding, Intelligence, and Automation (RWIA’2022) and the 14th Chinese Conference on Robotic Welding (CCRW’2022)”. It includes one feature article and five research papers. The feature article in this issue, titled “Sustainable Manufacturing Processes in the Automotive Industry: Acoustic Emission Proposal to Reduce Mechanical Testing Residues”, is contributed by Nachimani Charde Singgaran and Boris Miethlich. The article presents a comparative study that aims to understand the phenomena associated with two different electrode compressing mechanisms. The characterization results demonstrate a significant reduction in weld strength due to expulsions, even when they occur at a minimal level. The first research article, “Prediction of Cross-sectional Shape, Microstructure, and Mechanical Properties of Full Penetration Laser-GMAW Welded Butt Joints”, is contributed by Wei Naikun, Shi Jin, Yang Rundang, Xi Juntong, Gu Sheng, Luo Xiaomeng, Xu Aimin, Pan Huijun, Yang Shuai, Yu Chun, and Lu Hao. The paper utilizes finite element simulation and thermal dynamic simulation to investigate the formation feature dimensions, microstructure, and mechanical properties of full-penetration laser–GMAW-welded joints. By combining the simulations with the welding thermal cycle, it becomes possible to predict the microstructure and mechanical properties of the joints. The second research paper, entitled “Comparative Study on Thermal Generation and Weld Performances of Two Types of Micro-resistance Welding between Thick Multistrand Cu Wire and Kovar Interconnector with Different Electrode Systems”, is contributed by Guanzhi Wu, Nannan Chen, Zhichao Wang, Yi Wei, Jusha Ma, Min Wang, Chen Shen, Yuhan Ding, Yafei Pei, Bin Qian, and Xueming Hua. The paper introduces a redesigned electrode system for single-sided spot welding (SSSW) to match different welding conditions and achieve better welding quality. The research systematically compares the two welding processes both experimentally and numerically. The third research paper, titled “Forming Characteristics of Additive Manufacturing Process by Rotating Arc”, is contributed by Wenhang Li, Lin Lu, Qinglin Han, Jiayou Wang, Jianxin Wang, Rui Yu, Feng Yang, and Jie Zhu. This paper explores the application of rotating arc in additive manufacturing and conducts experiments to investigate the effect of process parameters on the surface forming quality of single deposition bead. Additionally, the impact of rotating frequency on the forming characteristics of the single-layer multi-bead additive deposition layer is also studied. The results shed light on the important factors influencing the forming characteristics of deposited layers. The fourth research paper in this issue, titled “A Fast Point Cloud Reconstruction Algorithm for Saddle-shaped Weld Seams in Boiler Header Joints”, is contributed by

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Editorial

Yang Lu, Huabin Chen, Mingzhen Rao, and Kai Liu. The paper proposes a fast point cloud reconstruction method for saddle-shaped welding seams in boiler header and tube-seat joints. The point clouds of header and tube-seat joints in boilers are acquired using a partition scanning strategy and then reconstructed using a two-step registration method. Additionally, the paper introduces a simple yet effective benchmark called the registration mark. The fifth research paper in this issue, titled “Feature Extraction and Classification Recognition of Molten Pool in Multi-layer and Multi-pass Welding of Medium and Thick Plates”, is contributed by Zhanying Xue, Hao Zhou, Runquan Xiao, Zhen Hou, Erbin Liu, Guobao Tang, and Shanben Chen. This article is based on a welding experiment involving multi-layer and multi-pass welding of medium and thick plates. It focuses on image processing and feature extraction of the weld pool, utilizing visual information as well as current and voltage information. The paper also examines the classification of the weld pool in four layers and seven passes. This issue of TIWM showcases new perspectives and developments in the field of intelligent welding research, as well as topics related to the RWIA&CCRW2022 conference. The publication of this issue is expected to inspire readers and provide them with fresh insights.

Yuming Zhang TIWM Editor-in-Chief

Contents

Feature Articles Sustainable Manufacturing Processes in the Automotive Industry: Acoustic Emission Proposal to Reduce the Mechanical Testing Residues. . . . . . . . . . . . Nachimani Charde Singgaran and Boris Miethlich

3

Research Papers Prediction of Cross-Sectional Shape, Microstructure and Mechanical Properties of Full Penetration Laser-GMAW Welded Butt Joints . . . . . . . . . . . Wei Naikun, Shi Jin, Yang Rundang, Xi Juntong, Gu Sheng, Luo Xiaomeng, Xu Aimin, Pan Huijun, Yang Shuai, Yu Chun, and Lu Hao Comparative Study on Thermal Generation and Weld Performances of Two Types of Micro-Resistance Welding Between Thick Multi-Strand Cu Wire and Kovar Interconnector with Different Electrode Systems . . . . . . . . . . . . . . Guanzhi Wu, Nannan Chen, Zhichao Wang, Yi Wei, Jusha Ma, Min Wang, Chen Shen, Yuhan Ding, Yafei Pei, Bin Qian, and Xueming Hua Forming Characteristics of Additive Manufacturing Process by Rotating Arc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenhang Li, Lin Lu, Qinglin Han, Jiayou Wang, Jianxin Wang, Rui Yu, Feng Yang, and Jie Zhu A Fast Point Cloud Reconstruction Algorithm for Saddle-Shaped Weld Seams in Boiler Header Joints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Lu, Huabin Chen, Mingzhen Rao, and Kai Liu Feature Extraction and Classification Recognition of Molten Pool in Multi-layer and Multi-pass Welding of Medium and Thick Plates . . . . . . . . . . Zhanying Xue, Hao Zhou, Runquan Xiao, Zhen Hou, Erbin Liu, Guobao Tang, and Shanben Chen

23

42

56

70

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Adaptive Ant Colony Algorithm Based on Global Scanning . . . . . . . . . . . . . . 107 Cui Can, Zhi Heng, Jiang Junnan, Tang Xiaoxiang, and Wang Xuewu

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ACB-RRT*: Adaptive Companion Points Bidirectional RRT* Algorithm . . . . . 126 Junnan Jiang, Heng Zhi, Xiaoxiang Tang, Can Cui, and Xuewu Wang Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Feature Articles

Sustainable Manufacturing Processes in the Automotive Industry: Acoustic Emission Proposal to Reduce the Mechanical Testing Residues Nachimani Charde Singgaran1(B) and Boris Miethlich2(B) 1 Advan-kt Multiversity Inc., Delaware, USA

[email protected]

2 Faculty of Management, Comenius University, Bratislava, Slovak Republic

[email protected] https://www.advan-kt.com

Abstract. Expulsion is a common happening in the spot welding process due to misalignment of process parameters which splashes out molten metals during the fusion process. The process parameters (welding current, welding cycle, electrode compressing force, and electrode tip diameter) play a crucial role in establishing a sound weld and therefore a comparative study is developed to understand the phenomena for two different electrode compressing mechanisms. Mild steel sheets are conventionally used to characterize the weld expulsion in 75KVA pedestal AC spot welding machines; by examining very light expulsion. The welded samples are then subjected to mechanical tests such as the tensile test, hardness test, and metallurgical observation. The welding processes are also electrically recorded for acoustic behaviors as well as the dynamic resistances. The signal can be used to determine the weld quality without engaging the destructive test. In overall, the characterization results have shown that the expulsions reduce the weld strength significantly; though it happened for very light expulsion. Keywords: Expulsion · Spot welding · Mild Steel · Macro Structure · Weld Strength · Dynamic Resistance · Acoustics

1 Introduction Expulsion mitigates the weld strength crucially and very few research papers are available in resistance spot welding to understand it. As such, several literature was initially reviewed before a new experiment is designed to carry out. Ma et al. (2006) have analyzed the expulsion effects very well through dynamic resistances computation but without metallurgical results. Farson et al. (2003) have also investigated the displacement curve of electrodes precisely with respect to expulsion but technically, it is hard to imagine the profound changes without macro-graphs for very mild expulsion. Kimchi (1984) too has conducted a preliminary study about the expulsion but lack of metallurgical © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. Chen et al. (Eds.): RWIA 2022, TIWM, pp. 3–20, 2024. https://doi.org/10.1007/978-981-99-9629-2_1

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views as well. In Sung et al. (2011) have included the metallurgical view up to macro graph levels but for the stainless steel. Primoz et al. (2004) analysis is limited to signal levels; Shen et al. (2010) analysis is limited to electrode contact radius with simulation technique; Al-Jader et al. (2009) investigation has ended up with millimeter scale of macro graphs. With all these researchers great contributions in mind, an experiment was conducted for the pneumatic-to-servo converted electrode actuating system in 75 kVA resistance spot welding machine, aiming to observe the acoustic patterns for very light expulsion, dynamic resistance changes during expulsion, macro-graph views as well as the metallurgical changes. Figure 1 illustrates the pneumatic-to-servo converted compressing mechanism while Fig. 2 illustrates the expulsion effect during a resistance spot welding process.

Fig. 1. Compressing pneumatic-to-servo converted system with PLC-controlling concept

Sustainable Manufacturing Processes in the Automotive Industry

Fig. 2. The expulsion effect during a resistance spot welding process

2 Experimental 2.1 Sample Size, Welding Alignment and Materials Properties

Fig. 3. Lap joint of carbon steel sheet with acoustic sensor

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Weldable samples are made from carbon steel sheets, with a rectangular shape of 200 mm length by 25 mm width from 1 mm thickness sheet (Nachimani 2016). Lap joints are developed so it can be easily tested in tensile shear test (Fig. 3). Carbon steel’s chemical properties are listed in Table 1 (Nachimani 2017). Table 1. Material’s properties of carbon steel Elements (%)

Fe

C

Mn

Si

S

P

Balance

0.18

0.90

0.006

0.050

0.040

2.2 Machine Modification, Welding Lobe Curve and Welding Schedule The entire welding processes are done through 240 VAC (75 kVA) and C-typed pedestal resistance spot welding machine (Fig. 1), having two types of different compressing mechanism. Truncated electrode tip of 5 mm diameter is selected from RWMA’s class two (copper and chromium) category and used throughout the entire welding welding process. Hence, the welding current (kA), compressing force (kN) and welding time (cycle) are varied to select from some good welds to expulsion regions. Figures 4 and 5 are illustrating the welding lobe curve for the pneumatic system while the Figs. 6 and 6 are illustrating the welding lobe curve for the servo system. As to generate light expulsion weld in any of the two systems, the servo based system was selected because the welding lobe curve is coincidentally overlapped in the pneumatic system. The selected welding schedules (combination of parametric set up) are marked with purple color ‘X’ in order to identify in the welding lobe curves. Based on it, the Table 2 was summarized.

Fig. 4. The welding lobe curve for the pneumatic system (welding current versus welding time)

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Fig. 5. The welding lobe curve for the pneumatic system (welding current versus compressing force)

Fig. 6. The welding lobe curve for the servo system (welding current versus welding time)

2.3 Signal Capture for the Welding Processes A welding process unleashes various acoustic signals intrinsically. Typically the electrical current flow because the magnitude will usually be in kilo Amperes but in listenable spectrum (typically around 50–60 Hz), dissimilar weld breakdown during fusion process, expulsion, screw movement from compressing electrode mechanism, eddy current flow from transformer’s secondary coil and etc.(Senkara et al. 2010). Figure 8 shows some of the signals that these machines captured during the welding process but smartly picked the right signals using right instrumentation. Thus, a low pass filter is inevitably connected after the acoustic sensor with a resistor-capacitor circuit by tuning the cutting frequency to max 60 Hz. Cutting frequency is tuned to 60 Hz, such as: fc = 1/2RC; where R represents the resistor and C represents the capacitor. The received signal was

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Fig. 7. The welding lobe curve for the servo system (welding current versus compressing force)

Table 2. Parametric combinations of welding process for two system. Sample No

Weld Schedule No

Electrode Tip (mm)

Current (kA)

Time (cycle)

Force (kN)

Welding current and welding time variation 1–7

1

5

6

20

3

8–14

2

5

7

16

3

15–21

3

5

8

12

3

Welding current and compressing force variation 22–28

4

5

6

10

3

29–35

5

5

7

10

4.5

36–42

6

5

8

10

6

electronically stored in excel files as magnitude versus time. This time signal is then converted to Fast Fourier Transform (FFT) and then to Fast Fourier Transform Complex (FFTC) which is aided to plot it graphically. The FFT (Complex) distinguishes the sound welds over the expulsion welds with a second amplitude present at an indication of the expulsions. Apart from the distorted acoustic signal, electrical voltage and electrical current were also captured during the welding processes. Later, these signals were used to compute the dynamic resistances from which a substantive decision was derived for expulsion.

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Fig. 8. Various acoustic signals that obtained during welding processes

2.4 Welding Process and Data Collection for Analysis Process variable are carefully selected to observe the expulsion welds over the good welds and therefore six points have been selected for. These points are marked with purple colored ‘X’ in the welding lobe diagrams, which are shown in Figs. 2(b, c, d, e). The normal procedure for a welding process is that the sample sheets need to be placed on the top of lower electrode tip, such a way that a lap joint is set and the upper electrode is thereafter compressed by initiating means. After attaining the sufficient compressing force despite the pneumatic or servo system, the welding current is enabled in accordance with the preset values (Table 2). Once the welding current is passed through electrodes, the compressing lever consumes very short time in its present position to allow solidification process. Only then the compressing lever will return to home position. With these repetitive steps: seven samples are welded for each of the weld schedule. First five pairs are used for averaging the tensile shear test’s result. The sixth one is used for hardness test and the final one is used for microscopic examination. While the process takes place, acoustic sensor is used to capture the behavioral pattern of acoustic signal, which is mounted on the welding samples surface. As for the hardness test and tensile test, the Rockwell hardness tester using scale B with 20kg of indentation and 100 kN tensile test machines are engaged to complete the tests. Standard procedures, including the cutting and polishing steps are followed in order to prepare the bakelite samples for micro and macro structural observation. Moreover, the V2A etchant which contains

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100 ml of water, 100 ml of hydrochloric acid and 10 ml of nitric acid is used to etch the well prepared and weld polished bakelite-samples for electron microscopic examination.

3 Results and Discussion 3.1 Welding Lobe Improvement In the pneumatic system, the welding current against welding time set (Fig. 4) has produced 23 good welds, but in the servo system (Fig. 6) it has been increased to 32 numbers of spots. When the welding current versus compressing force in pneumatic system is counted (Fig. 5), then just 12 good welds are noticed whereas the servo system has widen the margin and produced 15 good weld spots (Fig. 6). It is premature to select the weld schedule without computing the actual working limits as this paper set precedent for the computation of welding lobes. Servo based compressing mechanism shifts the working window in the welding lobe curves whereby the lower current consumption is achieved for both parametric setup as shown in Figs. 9 and 10. This is absolutely an improvement in engineering efficiency because the welding current requirement has been pulled down to lower levels. Literally the good welds are obtained with lower current dissipation as this factor will economically save the automotive industries when multiple spot welding machines are being operated simultaneously. From Figs. 9 and 10, it is obviously seen that good welds of pneumatic welding lobe curves have attained the expulsion regions of servo’s welding lobe curves. To justify the accuracy of this prediction, some welds are developed and the results being shown in the following section.

Fig. 9. a superimposed welding lobe curves of the pneumatic and servo system (welding current versus welding time)

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Fig. 10. a superimposed welding lobe curves of the pneumatic and servo system (welding current versus compressing force)

3.2 Sizing the Welds for Sound Welds Against Expulsion Welds According to the welding schedule no 1, 2, and 3 the welding current and welding time are increased in steps while keeping the force and electrode tips unchanged (Yi et al. 2016) (Chertov et al. 2007). This increment has caused the weld growth because the fundamental relationship is governed by the heat, Q = I2 Rt; where I represents the current, R represents the resistance and t represents the welding time (Chertov et al. 2007) (Luo et al. 2013). Figure 11 proves this phenomena for two different electrode compressing systems, though the parameters set up remained the same. The weld formation has been observed by acoustic emission which unleashed the welding current frequency (f = 1/t = 50 Hz) as the main lobe with damping lobes as side frequencies. When this process is disturbed by welding deficiencies like expulsion, then the main lobe is divided into more than one central frequency. Thus, this phenomena can be used to determine the quality of welds without destructing it as we are moving towards the industrial revolution 4.0. Breaking apart every welds is impractical for an approximate 5000 spot welded automotive body frame but measuring the acoustic emission can help to set minimum number

a) Pneumatic good weld

b) Expulsion servo weld

Fig. 11. Weld schedule 1 from Table 2

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of quality spot welds. From Figs. 12, 13, 14, 15 and 16, the welding schedule from 2 to 6 are analyzed for very light expulsion condition, in reference to Figs. 9 and 10. It should be noted here that the weld nugget about 3 mm can be produced for 1 mm carbon steel without knowing the internal condition if acoustic signals are not monitored.

a) Pneumatic good weld

b) Expulsion servo weld

Fig. 12. Weld schedule 2 from Table 2

a) Pneumatic good weld

b) Expulsion servo weld

Fig. 13. Weld schedule 3 from Table 2

a) Pneumatic good weld

b) Expulsion servo weld

Fig. 14. Weld schedule 4 from Table 2

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a) Pneumatic good weld

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b) Expulsion servo weld

Fig. 15. Weld schedule 5 from Table 2

a) Pneumatic good weld

b) Expulsion servo weld

Fig. 16. Weld schedule 6 from Table 2

3.3 Dynamic Resistance It should be questioned here, why this very light expulsion occurs for the same parametric set up, as many researchers believe force profiles are constant throughout the welding processes. To explore this quest, the conventional analysis of dynamic resistance is computed for all the welding processes. Figures 17 and 18 have plotted the dynamic resistances for a pneumatic system, in which the expulsion is completely avoided with the help of the proper working region of welding lobes (Fig. 9). Theoretically, the dynamic resistance is assumed to be, R = ρí/A; where the resistivity (ρ) of carbon steel is 10 x 10–7 .m, the maximum length of current flow is 2 x 10–3 m, over an area of A = πr2 = (3.14)(5 x 10–3 ) = 7.853 x 10–5 m2 . As a result, R is equal to 0.254 μ. This value is significantly varied during the fusion process due to the faying surfaces between 2 sheets of 1 mm carbon steel, and two electrode-to-metal contacts. Considering the changes from points 1 to 4 of Figs. 17 and 18, which are normally seen as the surface breakdown region where the contacts are well established. From points 9 to 16 of Figs. 17 and 18, the molten section increases until it can escape from the enclosed electrode caps, and from points 17 to 20 of Figs. 17 and 18, the electrode produces an indentation due to continued electrode pressure as the compressing system was pneumatic. Since the

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welding lobe diagram (Figs. 4, 5, 6, and 7) was predicted prior to welding schedule selection, the expulsion is utterly avoided.

Fig. 17. Dynamic resistance for the pneumatic system (weld schedule 1 to 3)

Fig. 18. Dynamic resistance for the pneumatic system (weld schedule 4 to 6)

In similar way, the light expulsion region is selected with the help of Figs. 6 and 7 for servo-system, which have to be in coherence with the pneumatic system. The results are plotted in Figs. 19 and 20 for the weld schedules (1 to 6) from Table 2. Considering the changes from points 1 to 4 of Figs. 19 and 20, which are normally seen

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Fig. 19. Dynamic resistance for the servo system (weld schedule 1 to 3)

as the surface breakdown region where the contacts are well established. These regions look good enough, but from points 10 to 12 of Figs. 19 and 20, the molten section generated expulsion, where the dynamic resistance obviously drops. The reasons for these changes are that the welding processes exceeded the parametric limits, which caused heat generation beyond acceptable limits. These are anticipated occurrences, as the welding lobe diagram proves it with its regions. The expulsion is well recorded in acoustic emission and from now on, a non-destructive test would be ideal to differentiate the sound weld over expulsion welds.

Fig. 20. Dynamic resistance for the servo system (weld schedule 4 to 6)

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3.4 Tensile Shearing Force of Welded Joints Shearing the welded pair by using a tensile machine is a way of analyzing the weld strength (Liu et al. 2017) (Umair et al. 2019) (Liu et al. 2017). Hence, a tensile shearing machine was used to accomplish the task with a setup of 50 mm derivation per minute. The test was carried out to investigate the crack initiations and therefore the cracking force was recorded. Figure 21 shows the tensile test results, in which the expulsion welds seemed to be producing poor joining strength as compared to the good welds. Increasing the welding current and welding time will definitely increase the weld bead and therefore more forces are drawn to crack it up (weld schedule 1 to 3). However, increasing the force exertion will result in a resistive drop which will cause a lower heat generation and subsequently mitigate the weld bead growth (weld schedule 4 to 6). These principles are

Fig. 21. Weld shearing forces for the corresponding weld diameters

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also seen in the light expulsion welds (servo system) and thereby the graphs illustrated a similar pattern except the magnitudes. 3.5 Metallurgical Study of a Sound Weld Over an Expulsion Weld Metallurgical analysis plays a crucial role in revealing macro, mezzo and micro structures and therefore it has been carried out to characterize the weld formation for the good weld over the light expulsion weld. It was unfortunate for the expulsion welds category to have unattractive macro graphs as compared to good welds, though the micro graph was almost the same for both categories. Thus, the expulsion occurrences do not influence the solidification process very much and therefore the micro graphs seemed to be almost the same between these two categories. Besides, the light-expulsion-welds strengths’ margin falls around 1kN lagging in 5kN overall scale. This can be visualized from Fig. 21 where the weld schedule 1 has drawn 5434 N force to crack the good weld (diameter 3.275 mm). On the contrary, the same parametric set up has caused expulsion in servo system welds for which the cracking force was 4234 N. Similar force exertion was seen for the entire Table 2 weld schedules (Fig. 22).

Fig. 22. a superimposed macro and micro graphs of good weld over expulsion weld

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3.6 Hardness of the Good Welds Over Expulsion Welds

Fig. 23. Hardness distribution across good weld joints

Hardness test determines the materials strength, because the solidification process of molten metal alters the crystalline structure of weld joints in RSW (Thornton et. al. 2012) (Luo et. al. 2013) (Macías et. al. 2015) (Ghafarallahi et. al. 2021). However, in this experimental study the micro level changes were not significantly happened due to the unchanged solidification process. Even then, the hardness of the expulsion weld is

Fig. 24. Hardness distribution across good weld joints

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reduced because the density of the nugget was reduced from the splash out activity. In other words, a small amount of molten metals has been splashed out from the weld bead which reduced the weld strength without altering the micro structure. Figures 23 and 24 show the hardness differences between the good welds (ave = 115 HRB) and expulsion welds (ave = 105 HRB); respectively.

4 Conclusion 1. The servo-based welding lobe diagram has increased the expulsion initiation from 5 mm to 5.50 mm of a weld diameter, which is an improvement for 5 mm electrode caps. This is one of the reasons why the production of good weld spots has increased to 32 spots from 23 spots as compared to a servo system over a pneumatic system. 2. Welding lobe curve shift has reduced the consumption of high electric current to weld metals and is economically a saving mechanism for the spot welding industry. 3. Light expulsion reduces the tensile strength and hardness of a spot weld without completely destroying the welds. 4. Acoustic emission during the welding processes unleashes sound waves, as it depicts two different wave forms for a good weld over an expulsion weld. A good weld has a single center frequency curve, while the expulsion weld has a center frequency with additional enhanced side lobes in frequency spectrum. 5. The changes in acoustic emission align with the dynamic resistances sudden drop during welding processes, which gives double confirmation about the weld defects. 6. Weld nugget growth of light expulsion has deformed macro graphs with slightly reduced strength. This observation should not be confused with totally deformed weld nuggets, though the outlook shows similarities. 7. Hardness values of the welded areas are increased from 65 to 115 HRB for the pneumatic system while reducing the mild expulsion welds to 105 HRB, slightly. This outcome was supported by micro graphs in which severe phase changes were not noticed at all. Competing Interests This research is funded by Advan-kt Education Inc. USA (www.advan-kt.com). The grant number is M7(38).

References Al-Jader, M.A., Cullen, J.D., Athi, N., and Al-Shamma, A.I.: Spot welding theoretical and practical investigations of the expulsion occurrence in joining metal for the automotive industry. In: Second International Conference on Developments in eSystems Engineering (2009) Chertov, A.M., Maev, R.G., Severin, F.M.: Acoustic microscopy of internal structure of resistance spot welds. IEEE Trans. Ultrason. Ferroelectr. Freq. ControlUltrason. Ferroelectr. Freq. Control 54(8), 1521–1529 (2007) Farson, D.F., Chen, J.Z., Ely, K., Frech, T.: Monitoring of expulsion in small scale resistance spot welding. Sci. Technol. Weld. Joining 8(6), 431–436 (2003)

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Ghafarallahi, E., Farrahi, G.H, Amiri, N.: Acoustic simulation of ultrasonic testing and neural network used for diameter prediction of three-sheet spot welded joints. J. Manuf. Process. 64, 1507–1516 (2021) In Sung, H., Mun, J.K., Dong, K.: Expulsion reduction in resistance spot welding by controlling of welding current waveform. Phys. Eng. 10, 2775–2781 (2011) Kimchi, M.: Spot weld properties when welding with expulsion—a comparative study. Welding J. February 63, 58 (1984) Lee, H.T., Wang, M., Maev, R.: A study on using scanning acoustic microscopy and neural network techniques to evaluate the quality of resistance spot welding. Int. J. Adv. Manuf. Technol. 22, 727–732 (2003) Liu, J., Xu, G., Ren, L., Qian, Z., Ren, L.: Simulation analysis of ultrasonic detection for resistance spot welding based on COMSOL Multiphysics. Int. J. Adv. Manuf. Technol. 93, 2089–2096 (2017) Liu, J., Xu, G., Ren, L., Qian, Z., Ren, L.: Defect intelligent identification in resistance spot welding ultrasonic detection based on wavelet packet and neural network. Int. J. Adv. Manuf. Technol. 90(9–12), 2581–2588 (2017) Luo, Y., Li, J., Wu, W.: Characterization of nugget nucleation quality based on the structure-borne acoustic emission signals detected during resistance spot welding process. Measurement 46, 1053–1060 (2013) Luo, Y., Li, J.L., Wu, W.: Nugget quality prediction of resistance spot welding on aluminium alloy based on structureborne acoustic emission signals. Sci. Technol. Weld. Joining 18(4), 301–306 (2013) Ma, C., Bhole, S.D., Chen, D.L., Lee, A., Biro, E., Boudreau, G.: Expulsion monitoring in spot welded advanced high strength automotive steels. Sci. Technol. Weld. Joining 11(4), 480–487 (2006) Macías, E.J., Roca, A.S., Fals, H.C., Muro, J.C.S., Fernández, J.B.: Characterisation of friction stir spot welding process based on envelope analysis of vibro-acoustical signals. Sci. Technol. Weld. Joining 20(2), 172–180 (2015) Nachimani, C., Ahmad, R., Abidin, N.I.Z.: Interpreting the weld formations using acoustic emission for the carbon steels and stainless steels welds in servo-based resistance spot welding. Int. J. Adv. Manuf. Technol. 86, 1–8 (2016) Nachimani, C.: Techniques for the improvement of carbon steels welds: under the SISF and DIDF welding schemes using pneumatic- and servo-based electrode actuating systems in resistance spot welding. Int. J. Adv. Manuf. Technol. 89, 3161–3168 (2017) Primoz, P., Ivan, P., Janez, D., Zoran, K.: Expulsion detection system for resistance spot welding based on a neural network. Meas. Sci. Technol. 15, 592–598 (2004) Senkara, J., Zhang, H., Hu, S.J.: Expulsion prediction in resistance spot welding: a model is proposed for predicting expulsion in resistance spot welding. Weld. J. 83(4), 123-S (2004) Shen, J., Zhang, Y.S., Lai, X.M.: Influence of initial gap on weld expulsion in resistance spot welding of dual phase steel. Sci. Technol. Weld. Joining 15(5), 386–392 (2010) Thornton, M., Han, L., Shergold, M.: Progress in NDT of resistance spot welding of aluminium using ultrasonic C-scan. NDT 48, 30–38 (2012) Umair, S., Xun, L.: Effects of ultrasonic vibration on resistance spot welding of transformation induced plasticity steel 780 to aluminum alloy AA6061. Mater. Des. 182, 108053 (2019) Yi, L., Rui, W., Xiaojian, X.: Expulsion analysis of resistance spot welding on zinc-coated steel by detection of structure-borne acoustic emission signals. Int. J. Adv. Manuf. Technol. 84, 1995–2002 (2016)

Research Papers

Prediction of Cross-Sectional Shape, Microstructure and Mechanical Properties of Full Penetration Laser-GMAW Welded Butt Joints Wei Naikun1,2 , Shi Jin2 , Yang Rundang2 , Xi Juntong1 , Gu Sheng2 , Luo Xiaomeng2 , Xu Aimin2 , Pan Huijun2 , Yang Shuai2 , Yu Chun3(B) , and Lu Hao3 1 School of Mechanical Engineer, Shanghai Jiao Tong University, Shanghai 200240, China 2 Shanghai Shipbuilding Technology Research Institute, Shanghai 200032, China 3 School of Materials Science and Engineering, Shanghai Jiao Tong University,

Shanghai 200240, China [email protected]

Abstract. Finite element simulation and thermal dynamic simulation were employed to investigate the formation feature dimensions, microstructure and mechanical properties of full penetration laser-GMAW welded joints. An ellipsoidal-conical hybrid heat source was established, and the thermal processes of hybrid welding for butt joint on a steel plate with 5, 10 and 14 mm thickness were predicted. The effects of arc power, laser power, and welding speed on the characteristic size at the cross section were investigated. It is found that the arc power only affects the size of upside, the laser power mostly determines the size of backside and the minimum width, and less effect on the width of upside, while the welding speed affects all the characteristic size of cross section. A relationship among phase composition, mechanical properties and cooling rate was established by thermal dynamic simulation. Combining the simulation of welding thermal cycle, the microstructure and mechanical properties of the joints can be predicted. This research is interesting and important for the design of process parameters during welding. Keywords: Laser-GMAW welding · finite element simulation · thermal dynamic simulation · full penetration · hybrid heat source

1 Introduction Laser-gas metal arc welding (GMAW) hybrid welding is a new kind of technology, it has been a potential welding technology in various industrial applications, like shipbuilding, automobile industry, et al. [1, 2]. The hybrid welding technology combines the advantages of both the laser welding and the GMAW [3, 4]. It is well known that Laser welding has energy with ultra-high density, it can realize deep penetration welding, and improve weld quality. On the other hand, because the energy density of GMAW process © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. Chen et al. (Eds.): RWIA 2022, TIWM, pp. 23–41, 2024. https://doi.org/10.1007/978-981-99-9629-2_2

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is relatively low, while the heating area is large, as a result, it can adapt a larger tolerance of assembly accuracy relative to laser welding. While the disadvantage of GMAW is that the welding efficiency is lower. The hybrid of laser and GMAW increases the penetration depth, bridge-ability and high welding speed, and hence enhances the welding productivity and quality. Since the advantages of the hybrid welding technology are obvious and significant, this new welding technology has attracted a lot of attentions in the recent years. Most works are emphasized on experimentally exploring the optimum process window for different materials or structures [5–7]. However, since the variables in the hybrid welding process are much more than the traditional arc welding or laser welding, the process development becomes more complex and expensive. With the development of welding simulation technology, the computation aided design method was employed to the development of new welding process. The formation parameters, such as width and depth of weldment are firstly considered in determining the welding process. Though the relation between formation parameters and process parameters can be established by a series of experiments, there is another cost-efficiency method, finite element thermal simulation [8–10]. While the accuracy of simulation is related with the heat source of welding. Various heat sources have been proposed in the literature, like classic double ellipsoidal heat source [11], and conical heat source [12]. However, these heat sources are difficult to simulate deep penetration welding by itself, hence, the combination of two or three volumetric heat sources is sometimes necessary to get accurate thermal fields [13–15]. On the other hand, the microstructure and mechanical properties of welding joint determine the quality and lifetime of the whole structures or products, an effective prediction method of microstructure and mechanical properties is also an emergency requirement when determining the welding process parameters. Therefore, in order to solve the problem of accuracy prediction and control of formation, microstructure and mechanical properties of the new laser-GMAW technology welded joints, both finite element simulation and thermal dynamic simulation were employed in this paper. An ellipsoidal-conical hybrid heat source was established, and finite element simulation was employed to investigate the formation of Laser-gas metal arc welding (GMAW) hybrid welding, the effects of key welding parameters, like laser power, arc power and welding speed, on the formation features were explored. A relationship among phase composition, mechanical properties and cooling rate was established by thermal dynamic simulation. Combining the simulation of welding thermal cycle, the microstructure and mechanical properties of the joints can be predicted, further guiding the design of process parameters in laser-GMAW.

2 Methods The thermal process of full penetration Laser-GMAW hybrid welding for steel plate with 10 mm in thickness was simulated by using finite element method, only considering heat conduction mode.

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2.1 Assumptions and Exclusions The main assumptions were made as following, (1) (2) (3) (4)

The material is isotropic and homogeneous. Laser-material interaction and absorption mechanisms are neglected. Fluid flow and convection in the melt pool is not considered. The zero present gap between the plates (butt joint seam) is excluded and the FE model is treated as bead-on-plate. (5) The filler material has similar material properties as the base metal. (6) Surface forces, plasma pressure, shielding gas pressure, and the weld reinforcements on the top and root sides are excluded. (7) the gap between the centers of laser and arc is 2 mm, and the angle between the laser and arc is 30°. 2.2 Thermal Conduction Model In thick plate welding process, the heat flow is three dimensional (3D). Accordingly, the temperature calculations in this work adopt the classical Fourier’s heat equation for 3D transient heat conduction [16]. This equation is given as,       ∂T ∂ ∂T ∂ ∂T ∂T ∂ λ + λ + λ = ρCp −q (1) ∂x ∂x ∂y ∂y ∂z ∂z ∂t where, ρ represents density, C p is specific heat capacity, λ is thermal conductivity, t represents welding time, x, y and z represent space coordinates, q is the volumetric internal energy generation, T is temperature. 2.3 Heat Source Model For the hybrid welding process, a hybrid volumetric heat source was established, which consisted of a ellipsoidal heat source and three double-conical heat sources for the arc part and the laser part, respectively. 2.3.1 Ellipsoidal Heat Source for Arc The ellipsoidal heat source can be mathematically expressed for a moving arc heat source in x direction using the following equation, √   2 y 2 z 2 6 3Q −3 ( x−vt a ) +( b ) +( c ) qA (x, y, z, t) = (2) √ e abcπ π In the equation, qA is the volumetric heat flux, Q is the effective heat input, Q = ηUI, where U is arc voltage, I is arc current, η is the efficiency and it was set as 0.75 is this calculation, x, y and z represent space coordinates, v is the welding speed along x direction, and a, b, c are heat source parameters.

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2.3.2 Conical Heat Source for Laser The conical heat source model can be mathematically expressed for a moving laser in x direction as following, 

 3 (x − vt)2 + y2 9QL e3  exp −   qL =  3 (3) rc2 π e − 1 (Ze − Zi ) re2 + re ri + ri2 QL = ηP rc = f (z) = ri + (re − ri )

(4) z − zi ze − zi

(5)

where, qL is the laser heat flux, ze , zi , r e and r i represent the z-coordinates and radii of the top and bottom surfaces, respectively. r c is the distribution parameter, e is the base of natural logarithm, QL represents the effective laser power, ηL is the laser heat efficiency, 0.8 in this calculation (Fig. 1).

Fig. 1. Sketch map for conical heat source

2.3.3 Hybrid Heat Source According to the cross-sectional morphology of laser-GMAW welding joint, a tribleconical heat source model should be developed to simulate the full penetration welding. As shown in Fig. 2, the whole heat source consists of three part, near the top surface, the heat is contributed from both arc and laser, it has an inverted-trapezoidal shape; in the middle and bottom part, the heats are mostly contributed from laser, and the middle part has a rectangle shape, while the bottom part has a trapezoidal shape. The hybrid heat source can be mathematically expressed by Eq. (6),

Prediction of Cross-Sectional Shape, Microstructure and Mechanical Properties

q Zf Zm1

Arc source

4$

rf

r1

4/

rm1

4/

r2 Zm2 Zb

27

Laser source

rm2 r3 rb

Fig. 2. Sketch map of laser-MIG hybrid heat source for full penetration welding

⎧ ⎨ qA + qL1 top surface q = qL2 middle part ⎩ bottom qL3

(6)

where, q is the total heat flux, qL1 is the laser heat flux affected on the top surface, qL2 is the laser heat flux affected on the middle part, and qL3 is the laser heat flux affected on the bottom of specimen, √   2 y 2 z 2 6 3QA −3 ( x−vt a ) +( b ) +( c ) qA = (7) √ e abcπ π 

 3 (x − vt)2 + y2 9QL1 e3  exp − qL1 =  (8)   r12 π e3 − 1 Zf − Zm1 r 2 + rf rm1 + r 2 f

m1



 3 (x − vt)2 + y2 9QL2 e3  exp −   2 qL2 =  3 (9) 2 π e − 1 (Zm1 − Zm2 ) rm1 + rm1 rm2 + rm2 r22 

 3 (x − vt)2 + y2 9QL3 e3   exp −  2 qL3 =  3 (10) π e − 1 (Zm2 − Zb ) rm2 + rm2 rb + rb2 r32 Hence, the hybrid heat source for full penetration laser-GMAW welding can be mathematically expressed by ellipsoidal- conical hybrid heat source,

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 ⎧ √

   2 y 2 z 2 3 (x − vt)2 + y2 6 3QA −3 ( x−vt ⎪ 9QL1 e3 ⎪ a ) +( b ) +( c ) ⎪   exp − + e √ ⎪    ⎪ 2 abcπ π r12 ⎪ π e3 − 1 Zf − Zm1 rf2 + rf rm1 + rm1 ⎪ ⎪ ⎪ ⎪  ⎪

 ⎨ 3 (x − vt)2 + y2 9QL2 e3 q=     exp − 2 2 ⎪ 3 ⎪ r22 ⎪ π e − 1 (Zm1 − Zm2 ) rm1 + rm1 rm2 + rm2 ⎪ ⎪  ⎪

 ⎪ 2 ⎪ 3 (x − vt) + y2 9QL3 e3 ⎪ ⎪ ⎪   2  exp − ⎩  3 π e − 1 (Zm2 − Zb ) rm2 + rm2 rb + rb2 r32

(11) in which,  Zf − Z  ) r1 = rm1 + rf − rm1 Zf − Zm1 Zm1 − Z ) Zm1 − Zm2

(13)

Zm2 − Z ) Zm2 − Zb

(14)

r2 = rm2 + (rm1 − rm2 ) r3 = rb + (rm2 − rb )

(12)

QL1 =

Zf − Zm1 Zf − Zb

(15)

QL2 =

Zm1 − Zm2 Zf − Zb

(16)

QL3 =

Zm2 − Zb Zf − Zb

(17)

where, QA is the effective arc power, QL1 , QL2 , and QL3 represent the effective laser power applied on the surface, the middle and the bottom parts of the specimen, respectively. r 1 , r 2 , and r 3 is the distribution parameters for the laser heat impacted on the three parts of specimen, respective. zf , zm1 , r m1 and r f represent the z-coordinates and radii of the top and bottom surfaces for the laser heat flux affected on the top surface, respectively. zm1 , zm2 , r m1 and r m2 represent the z-coordinates and radii of the top and bottom surfaces for the laser heat flux affected on the middle part of the specimen, respectively. zm2 , zb , r m2 and r b represent the z-coordinates and radii of the top and bottom surfaces for the laser heat flux affected on the bottom part of the specimen, respectively. In addition, the initial temperature and convective coefficient was set to 25 °C and 10e−6 W/(m2 ·°C), respectively. And the temperature dependent thermal conductivity and specific heat capacity were plotted in Fig. 5 [17] (Fig. 3).

Prediction of Cross-Sectional Shape, Microstructure and Mechanical Properties

2000

Thermal conductivity (W/m Specific heat capacity (J/Kg

Thermal physical properties

1800

29

) )

1600 1400 1200 1000 800 600 400 200 0

0

500

1000

1500

2000

Temperature ( Fig.3. Thermal physical properties as a function of temperature

2.4 Numerical Model Specimens with dimensions of 120 mm × 240 mm × 10 mm were used for full penetration welding simulation. Three-dimensional solid elements, solid70, with eight point quadrature integration were used for the model. The element sizes were determined by adjusting the resolution and accuracy of the temperature distribution in the regions of severe thermal gradients (Fig. 4).

Fig. 4. Finite element model for the 10 mm thick plate

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2.5 Thermal Dynamic Simulation Thermal dynamic simulation was carried out by JmatPro software, the evolution of microstructure and mechanical properties with different temperature and cooling rate can be calculated by the specific mode.

3 Simulation Results 3.1 Change of Cross Section Geometric Characteristics with Welding Parameters Figure 5 is the contour plot of cross-sectional temperature field for the butt joint with 10 mm in thickness. The applied welding parameters are arc power of 7400 W, efficiency is 0.75, laser power of 13500 W, efficiency of 0.8, the distance and angle between laser and arc are 2 mm and 30°, respectively. Welding speed is 2400 mm/min, the reference temperature is 20 °C. The energy mostly concentrates at the place near the heat source due to the ultra-quick process. Its shape can be divided into three parts, namely, inverted trapezoidal in the upside, rectangle in the middle part, and trapezoid in the backside, corresponding to the distribution morphology of the hybrid heat source. There are three geometrical formation parameters for the cross section, as shown in Fig. 5a, namely, width of upside, width of bottom, and minimum width. Assuming that the melting point of the material is 1450 °C, the feature sizes of the cross section can be presented and calculated. Figure 5b shows the comparison of calculated and experimental morphologies of cross section, they have a good agreement in shape. Moreover, the simulated and experimental characteristic dimensions, width of the upside pool, width of the backside pool and the minimum width of the weld, are listed in Table 1. It is seen that the values are very close, and the errors are less than 5%. It verifies that the simulation is reliable. Therefore, this heat source was further applied in the investigation of effects of welding parameters, like arc power, laser power, and welding speed on characteristic shape parameters of cross section.

(a) cross-sectional temperature plot

(b) comparison

Fig.5. Cross-sectional temperature plot for 10 mm plate and comparison to experimental result

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Table 1. Characteristic width comparison between simulation and experiment

experimental results width of the upside 4 pool width of the 2 backside pool minimum width of 1.12 the weld

simulated results 3.87

errors 3.25%

1.96

2%

1.07

4.46%

3.1.1 Effects of Arc Power on Characteristic Dimensions of Cross Section In this section, the effects of arc power, laser power, welding speed on the characteristic dimensions of cross section of weldment are investigated. Table 2 lists the process parameters used in the simulations, the arc power was changed while the other parameters were kept constant in this section. The contour plots of the cross-sectional temperature field were presented in Fig. 6. It is seen that there is no obvious change of the basic shape as change of arc power, while the width of upside of the cross section gradually increases with the increase of arc power. Table 2. Welding parameters for simulating the effects of arc power.

Arc power (W) Laser power (W) Welding speed (mm/min) 6500 13500 2400 7000 13500 2400 7400 13500 2400 8000 13500 2400 8400 13500 2400 9000 13500 2400

Angle of arc (°) 30 30 30 30 30 30

The value of the width of upside pool, width of backside pool and minimum width of the pool at the cross-sectional image as a function of arc power are plotted in Fig. 7, as well, the data were fitted linearly. It is seen that the upside width is linearly related to the arc power, while the width of backside pool and minimum width basically keep constant as the arc power changes. Because the welding speed is as high as 2400 mm/min, even the arc power has a large range, 6500 ~ 9000 W, the upside width increases slowly, the slope is 2.31 × 10–4 mm/W, it means that as the arc power increases 1000 W, the upside width increases only 0.23 mm.

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(a) arc power 6500 W

(b) arc power 7000 W

(c) arc power 7400 W

(d) arc power 8000 W

(e) arc power 8400 W

(f) arc power 9000 W

Fig. 6. Cross-sectional temperature contour plot at different arc power.

Prediction of Cross-Sectional Shape, Microstructure and Mechanical Properties

Characteristic dimensions (mm)

4.5

width of upside

width of bottom

33

minimum width

4.0 slope1=2.3e-4

3.5 3.0 2.5 2.0

slope2=0 1.5 slope3=0 1.0 6500

7000

7500 8000 Arc power (W)

8500

9000

Fig. 7. Relation between cross-sectional characteristic width and arc power.

3.1.2 Effects of Laser Power on Characteristic Dimensions of Cross Section Table 3 lists the simulation parameters that only the laser power was changed, and the cross-sectional temperature contour plots are presented in Fig. 8. It is seen that there is also no obvious effect on the basic shape of the cross section, while the width of bottom and minimum width gradually increase with the increase of laser power. The values of the width of upside, width of backside and minimum width at the cross-sectional image as a function of laser power are plotted in Fig. 8, as well, the data were fitted linearly. It is seen that the all the considered feature widths are positively linearly related to the laser power. The slopes for the width of upside, width of backside Table 3. Welding parameters for simulating the effects of laser power. Arc power (W)

Laser power (W)

Welding speed (mm/min)

7400

13500

2400

7400

13000

2400

7400

14000

2400

7400

14500

2400

7400

12500

2400

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(a) laser power 12500 W

(b) laser power 13000 W

(c) laser power 14000 W

(d) laser power14500 W

Fig. 8. Cross-sectional temperature contour plot at different laser power.

and minimum width are 1.5 × 10–4 mm/W, 2.84 × 10–4 mm/W and 1.88 × 10–4 mm/W, respectively. The results mean that as the laser power increases, cross-sectional widths of the pool increase. However, the width increases only around 0.2 mm per 1000 W laser power increase. 3.1.3 Effects of Welding Speed on Characteristic Dimensions of Cross Section Table 4 lists the welding process parameters for the simulation of temperature field that only the welding speed was changed, and the temperature contour plots are presented in Fig. 10. It is seen that the change is obvious, all the cross-sectional characteristic widths of the weldment gradually decrease with the increase of welding speed (Fig. 9). The values of the width of upside, width of backside and minimum width of the cross section as a function of welding speed are plotted in Fig. 11, as well, the data were fitted linearly. It is seen that the all the considered feature widths are positively linearly related to the laser power. The slopes for the width of upside, width of bottom and minimum width are -1.33 × 10–3 mm/(mm/min), -1.06 × 10–3 mm/(mm/min) and -1.2 × 10–3 mm/(mm/min), respective. The results mean that as the welding speed increases, all the cross-sectional width of the pool increase. However, the width increases only around

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35

Table 4. Welding parameters for simulating the effects of welding speed. Arc power (W)

Laser power (W)

Welding speed (mm/min)

7400

13500

1200

7400

13500

1500

7400

13500

1740

7400

13500

1800

7400

13500

2040

7400

13500

2100

7400

13500

2400

7400

13500

2520

7400

13500

2700

7400

13500

3000

4.5

width of upside

width of bottom

minimum width

Characteristic dimensions (mm)

4.0 slope1=1.5e-4

3.5 3.0 2.5

slope2=2.8e-4

2.0 1.5 slope3=1.9e-4 1.0 12500

13000

13500 14000 Laser power (W)

14500

Fig. 9. Relation between cross-sectional characteristic width and laser power.

0.1 mm per 100 mm/min speed increase. In addition, as the welding speed exceeds 2500 mm/min, the minimum width is as low as around 0.6 mm, it is possible that the actual full penetration welding process could not be realized.

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(a) welding speed 1200 mm/min

(b) welding speed 1500 mm/min

(c) welding speed 1800 mm/min

(d) welding speed 2100 mm/min

(e) welding speed 2400 mm/min

(f) welding speed 2700 mm/min

Fig. 10. Cross-sectional temperature contour plot at different welding speed.

Prediction of Cross-Sectional Shape, Microstructure and Mechanical Properties

Characteristic dimensions (mm)

6

width of upside

width of bottom

37

minimum width

5 slope1=-1.3e-3 4 3 slope2=-1.0e-3 2 1

slope3=-1.2e-3

0 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 Welding speed(mm/min) Fig. 11. Relation between cross-sectional characteristic width and welding speed.

3.2 Prediction of Microstructures and Mechanical Properties The continuous cooling transformation (CCT) curve and isothermal transition diagram (TTT) can be calculated by employing JmatPro software. As well, the phases with different cooling rate can also be predicted. For AH36 steel, the possible microstructures consists of austenite, ferrite, pearlite, bainite and martensite, mainly determined by the cooling rate during welding. Figure 12 presents the phase compositions as the cooling rates are 10 and 100 °C/s. At a relatively low cooling rate, the phase compositions are mostly composited by ferrite and bainite, with as the cooling rate is very high, the phase will be completely constituted by martensite. The mechanical properties of material mostly contribute to the microstructure. And the mechanical properties can also be predicted by the software, corresponding to the phase composition. Figure 13 clearly shows the change of phase compositions and mechanical properties with cooling rate. It supplied a guidance for process parameters design to get required microstructures and mechanical properties.

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(a) Cooling rate 10 /s

(b) Cooling rate 100 /s

Mechanical properties

Phase

Vol%

Fig. 12. The phase composition with different cooling rate.

           

Ferrite Pearlite Bainite Martensite

Yield strength (MPa) Hardness (HV)

    





   cooling rate

 /s









Fig. 13. Change curves of phase composition and mechanical properties with cooling rate.

4 Discussions Due to the high efficiency and relatively low cost, Laser-GMAW hybrid welding technology has been attracting a lot of attentions. However, as a new technology, there are a lot of unknows. Thereinto, the geometric shape of weld, microstructure, and mechanical

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39

properties are important features determining the weld quality, they are also significant factors when determining the welding parameters. In this work, except the steel plate with 10 mm in thickness, the welding temperature field for 5 and 14 mm thick plate were also simulated, the cross-sectional temperature field contours are shown in Fig. 14. The shapes of cross-sectional temperature field can also be divided into three parts, namely, inverted trapezoidal in the upside, rectangle in the middle part, and trapezoid in the backside, nearly same as that for 10 mm thick plate. Moreover, the relationships between characteristic geometrical formation parameters and arc power, laser power and welding speed are also same as that of the 10 mm plate, the arc power mostly affects the width of upside, while the laser power and welding speed affect all the three width at the cross section.

(a) 5 mm thick

(b) 14 mm thick

Fig. 14. The cross-sectional temperature field of 5 mm (5200 W arc and 7500 W laser, 2500 mm/min welding speed) and 14 mm (7400 W arc and 14700 W laser, 1850 mm/min welding speed).

Furthermore, the cooling rate can also be calculated by the thermal simulation. Then, different cooling rate can be obtained by adjusting the welding conditions. As a result, the microstructure and mechanical properties can be controlled and designed. Figure 15 shows the thermal cycle curves of the butt joint with 5 mm thickness, the welding process parameters are same, except for the preheat condition. And the cooling rate can be calculated by considering the time of cooling from 800 to 500 °C. The cooling rate is around 100 °C/s when no preheat, while the cooling rate decreases to around 50 °C/s as the preheat temperature is 150 °C. According to Fig. 13, the microstructure and mechanical properties can be predicted. These are interesting and important data for design of welding process parameters.

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Fig. 15. Thermal cycle curve during welding with different preheat.

5 Conclusions In this work, an ellipsoidal-conical hybrid heat source was built to simulate the temperature field of full penetration butt joint with laser-GMAW hybrid welding technology by employing finite element method, the thickness of steel plate was 5, 10 and 14 mm. And the effects of key welding parameters on the temperature distribution, shape parameters of the cross-sectional weldment were analyzed, the simulation results were verified by experimental results, and the error is less than 5%. In addition, the microstructure and mechanical properties of the weld were predicted by combining thermal calculation and JmatPro. The simulation could supply a guide for the design of welding process parameters, and the following conclusions were drawn, (1) The hybrid heat source consists of ellipsoidal heat source for arc welding and conical heat source for laser, and a trible-conical heat source model was established to simulate the full penetration laser welding. (2) Arc power affects the width of upside, the width increases with increase of arc power, while it does not affect the width of bottom and minimum width. All the cross-sectional widths increase with the laser power, while they decrease with the welding speed. (3) The microstructure and mechanical properties can be calculated by combining thermal calculation and JmatPro.

Reference:s 1. Yang, X., Chen, H., Zhu, Z., Cai, C., Zhang, C.: Effect of shielding gas flow on welding process of laser-arc hybrid welding and MIG welding. J. Manuf. Process. 38, 530–542 (2019) 2. Shiwei, Z., Junhao, S., Minhao, Z., Lin, Z., Pulin, N., Zhuguo, L.: Fiber laser welding of HSLA steel by autogenous laser welding and autogenous laser welding with cold wire methods [J]. J. Mater. Process. Tech. 275, 116353 (2020) 3. Atabaki, M.M., Ma, J., Liu, W., Kovacevic, R.: Pore formation and its mitigation during hybrid laser/arc welding of advanced high strength steel. Mater. Des. 67, 509–521 (2015)

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4. Zhan, X., Zhao, Y., Liu, Z., Gao, Q., Bu, H.: Microstructure and porosity characteristics of 5A06 aluminum alloy joints using laser-MIG hybrid welding. J. Manuf. Process. 35, 437–445 (2018) 5. Atabaki, M.M., Ma, J., Yang, G., Kovacevic, R.: Hybrid laser/arc welding of advanced high strength steel in different butt joint configurations. Mater. Des. 64, 573–587 (2014) 6. Bunaziv, I., Akselsen, O.M., Salminen, A., Unt, A.: Fiber laser-MIG hybrid welding of 5 mm 5083 aluminum alloy. J. Mater. Process. Technol. 233, 107–114 (2016) 7. Atabaki, M.M., Yazdian, N., Kovacevic, R.: Hybrid laser/arc welding of thick high strength steel in different configurations. Adv. Manufact. 6, 176–188 (2018) 8. Lindgren, L.E.: Numerical modelling of welding. Comput. Methods Appl. Mech. Eng. 195(48–49), 6710–6736 (2006) 9. Rao, Z.H., Liao, S.M., Tsai, H.L.: Modelling of hybrid laser-GMA welding: review and challenges. Sci. Technol. Weld. Joining 16, 300–305 (2011) 10. Dal, M., Fabbro, R.: An overview of the state of art in laser welding simulation. Opt. Laser Technol. 78, 2–14 (2016) 11. Goldak, J., Chakravarti, A., Bibby, M.: A new finite element model for welding heat sources. Metall. Trans. B 15, 299–305 (1984) 12. Goldak, J., Bibby, M., Moore, J., House, R., Patel, B.: Computer modeling of heat flow in welds. Metall. Trans. B 17B, 587–600 (1986) 13. Bendaoud, I., et al.: The numerical simulation of heat transfer during a hybrid laserMIG welding using equivalent heat source approach. Opt. Laser Technol. 56, 334–342 (2014) 14. Rahman Chukkan, J., Vasudevan, M., Muthukumaran, S., Ravi Kumar, R., Chandrasekhar, N.: Simulation of laser butt welding of AISI 316L stainless steel sheet using various heat sources and experimental validation, J. Mater. Process. Technol. 219, 48–59 (2015) 15. Farrokhi, F., Endelt, B., Kristiansen, M.: A numerical model for full and partial penetration hybrid laser welding of thick-section steels. Opt. Laser Technol. 111, 671–686 (2019) 16. Nguyen, N.T., Mai, Y.W., Simpson, S., Ohta, A.: Analytical approximate solution for double ellipsoidal heat source in finite thick plate, Weld. J. 83, 82s–93s (2004) 17. Tsai, Y.L., Chang, C.C., Chou, C.P.: Finite element analysis of the residual stress in butt welds of similar and dissimilar steel plates. Int. J. Comput. Mater. Sci. Surf. Eng. 4 130–142 (2011)

Comparative Study on Thermal Generation and Weld Performances of Two Types of Micro-Resistance Welding Between Thick Multi-Strand Cu Wire and Kovar Interconnector with Different Electrode Systems Guanzhi Wu1 , Nannan Chen1(B) , Zhichao Wang2 , Yi Wei2 , Jusha Ma2 , Min Wang1 , Chen Shen1 , Yuhan Ding1 , Yafei Pei1 , Bin Qian2 , and Xueming Hua1(B) 1 Shanghai Key Laboratory of Materials Laser Processing and Modification, School of

Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China {cnsjtu,xmhua}@sjtu.edu.cn 2 Shanghai Institute of Space Power Sources, Shanghai 200245, China

Abstract. The joining quality of welds between Kovar interconnectors and multistrand copper wires is crucial for the energy efficiency and operating life of solar arrays in space orbits. High-power solar arrays demand reduced circuit resistance by increasing the thickness of copper wires. However, it is challenging to obtain satisfied joining between the interconnector and thick wires with a core diameter above 200 µm by traditional single-side double spot welding (SDSW). In this case, single-side single spot welding (SSSW) with a redesigned electrode system is introduced to match different welding conditions and achieve better welding quality. In this research, the difference between the two welding processes is systemically compared experimentally and numerically. The over-heated electrode/wire interface and under-heated wire/interconnector interface found in the SDSW joints are significantly reduced in the SSSW joints. Attributing to the rebuilt welding current path, more Joule heat generates at the wire–interconnector interface, which reduces the thermal gap between the upper surface and the interface of weld and hence achieved enhanced weld interface and lowered thermal damage. The SSSW also improves the temperature within the cores, which improves the bonding quality between the wire filaments by filling the gaps between wire filaments with Ag-Cu eutectics and solid solutions. Keywords: Design of electrode system · Kovar interconnector · Multi-strand copper wire · Advanced welding system · Numerical simulation · Welding qualities

1 Introduction Solar arrays as the main energy source of satellites and space stations, play an extremely important role in aerospace engineering. Multiple solar cells stack in series to form solar arrays [1–3]. In this facility, solar cells are bonded with interconnectors, and the connection between the interconnector and power transmission wire is a crucial part of the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. Chen et al. (Eds.): RWIA 2022, TIWM, pp. 42–55, 2024. https://doi.org/10.1007/978-981-99-9629-2_3

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entire power system [4, 5]. These connections always operate under harsh environments e.g., atomic oxygen erosion, temperature cycling and space radiation [6–8]. The reliability and stability of the joint have a significant effect on the performance and safety operation of solar arrays [9]. Kovar is an iron-nickel-cobalt alloy that can resist atomic oxygen. Its linear expansion coefficient is close to that of ceramics and glass, and it is often used as electronic packaging material in the electronics and communication industries [10]. However, it is difficult to use as the interconnector for the solar cell array due to its poor connectivity with the silver layer of the solar cell [11]. Therefore, by means of silver plating on the surface, the weldability is improved. Since the resistivity of Kovar is much higher than that of silver, the thermal condition of Ag-coated Kovar materials during the welding process will be different from that of silver interconnects. Besides, the Ag coating can prevent the failure of Cu wire blow -130 °C [12]. In micro-welding technology, laser welding and brazing are common ways of fabricating solar arrays [13, 14]. However, it is easy to form intermetallic compounds with comparatively low efficiency. Therefore, the development of the fast-forming process for solar arrays is imperative. Owing to the sufficient joule heat, high efficiency (about ms) and concentrated heating zone, the single-side double spot welding (SDSW, also namely parallel gap resistance welding) process has attracted increasing attention [15– 17]. The three main welding parameters of SDSW are welding current, welding time and pressure of electrodes. By means of controlling these parameters, it is able to avoid the common welding problems of over-melting, spattering, interconnects or electrode breakage, etc., in order to improve welding reliability [18–20]. Ding et al. [21] reported that the bonding strength is mainly affected by the welding voltage and electrode force, while the welding time has little effect. Wang et al. [22] reported that interfacial phases mainly are Cu-based solid solutions and IMCs in PGRW of Al wire to Au/Cu pad. Ding et al. [23] analyzed the joining mechanism of dissimilar interfaces between the solar cell and Ag interconnector controlled by heat input of SDSW, but the welding process needs to consider the integrity of the solar cell. It is shown that too much or too little heat input will decrease the welding performance. In this consideration, numerical simulation is utilized to investigate the thermal and mechanical behavior during the welding process. Zhan et al. [24] developed a thermal- electrical-mechanical finite element model for SDSW of Germanium-based solar cells and Ag interconnectors. The results indicated that the interdiffusion and recrystallization connection of SDSW is generated by the pressure of electrodes and the resistance heat. Similar models of SDSW are also investigated by Wu et al. [25] and Zeng et al. [26], and reveal that the deformation of welding area increases with the increasing of welding time due to the combined effect of external load and temperature. Moreover, Zhang et al. [27] achieved the joining of 200 µm AuNi9 microwires with a 3 µm Au layer via the SDSW method and utilized finite element simulation to calculate the welding temperature. They pointed out that the re-solidification of melted wire can help obtain a robust joint by optimizing the welding current. The ability of SDSW in joining thin wires has been proved by above-mentioned researches. However, due to the characterize of SDSW technique and high conductivity of Cu and Ag, the welding quality for joining thick Cu wires still needs to be determined.

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In this study, an advanced SSSW technique is developed as a candidate to join thick wires by redesigning the electrode system. The mechanical and electrical properties of joints fabricated by both techniques under various welding voltages are systemically investigated. The microstructure features and element distribution of joints under typical parameters are also observed. Afterwards, the numerical simulation is utilized to illustrate the thermal and electrical behavior during the welding process, and ultimately reveal the relationship between the microstructure and properties in both welding techniques.

2 Experimental Procedure and Numerical Simulation 2.1 Welding Processes Ag-coated Cu wires of different sizes are used in this study, which is significant in the solar array to transfer electric power. The multi-strand Cu wire consists of 19 Cu filaments with a diameter of 203 µm and coated by 1 µm thick Ag. The thickness of Kovar interconnector is about 50 µm. Ag is plated on both sides of the Kovar interconnector with a thickness of 5 µm. The NRW-IN400PA welding source is utilized to finish the welding process. Figure 1(a) and (c) exhibit the schematics of both single-side double spot welding

Fig. 1. Schematics of welding systems and typical appearance of joints: (a) SDSW system, (b) appearance of the SDSW joint, (c) SSSW system, and (d) appearance of the SSSW joint.

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(SDSW) and single-side single spot welding (SSSW) respectively. The SDSW technique is equipped with two parallel Mo electrodes, while the SSSW technique contains three parts, namely the welding electrode (electrode #1), assist electrode (electrode #2) and conductor (Mo plate). The width of the electrode tip is 1 mm for each welding process. The Cu wire and Kovar interconnector are joined by both welding techniques under the equivalent electrode force and various welding voltages. In order to prevent thermal damage of the Mo electrode and Cu wires, the welding voltages of SDSW and SSSW for these copper wires are 2.0–2.6V and 1.6–2.4V, respectively. Many steps are involved for these, including electrode force loading, welding voltage loading, welding, welding voltage unloading and cooling. Based on our previous study, the force loading and cooling period are both nearly 0.2 ms as well as the welding time, voltage loading and unloading periods are chosen as about 15 ms. Figure 1(b) and (d) are the typical appearances of SDSW and SSSW joints. 2.2 Assessments of Mechanical and Electrical Properties To systemically evaluate the welding quality of welded joints, the mechanical and electrical properties under various welding voltages are measured. The mechanical properties are evaluated by tensile testing and the electrical properties are assessed by measuring the joint resistance with the four-probe method. The peak load is investigated on a JHY-5000 electronic universal testing machine, and the resistance is obtained via the CXT2515 small electric resistance instrument. In this study, 6 joints of each welding parameter are adopted and the average value is taken. Figure 2 shows the schematic of the resistance measurement of welded joints.

Fig. 2. Schematic of the resistance measurement of welded joints.

2.3 Characterization of the Welding Joints To better reveal the relationship between the properties and microstructure, typical welded joints are investigated. An optical microscope (OM, Zeiss Axio A2m) is applied to display the macrostructure, and a scanning electron microscope (SEM, NOVA

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NanoSEM 230) is utilized to observe the connection interfaces via secondary electron image, EDS mapping and electron back-scattered diffraction (EBSD). 2.4 Simulation of Welding Processes The ABAQUS software is used to simulate and analyze the temperature and current density evolution during the welding process of both welding techniques. This simulation involves three coupling fields i.e., electricity, thermal and force, which should adopt the coupled thermal-electrical-structural model. The numerical simulation results can help reveal the characteristic of microstructure in the welded joints. As shown in Fig. 3, finite models for SDSW and SSSW are established based on the actual welding equipment. Attributing to the symmetry of these welding methods, both models are simplified to half to improve the speed of simulation. Figure 3(a) shows the actual model of SDSW, and the number of elements is 72032 and the number of nodes is 141803, while 174432 elements and 407493 nodes are in the SSSW model (Fig. 3(b)). To ensure the accuracy of these models, the element size in electrodes, Cu wire and Kovar interconnector is set to similar (illustration in Fig. 3(a)). The 19 filaments of the Cu wire in these models are equivalent to the geometry feature of the actual wire after the preloading stage, as shown in the set of Fig. 3(b). Correspondingly, the welding voltage, welding time and electrode force are the same as the welding process mentioned above.

Fig. 3. Finite models for (a) SDSW and (b)SSSW.

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3 Results and Discussion 3.1 Thick Wires Welded by SDSW and SSSW Techniques 3.1.1 Mechanical and Electrical Properties Our previous study proves that the traditional SDSW technique used to join solar cells and interconnectors can obtain good quality [28]. Similarly, the SDSW technique is adopted to join the Cu wire and interconnector. However, the thick wires bring higher resistance and lower thermal conductivity during the welding process, and therefore the traditional welding parameters should be adjusted to satisfy the requirement of welding quality. The peak load of the tensile test and weld resistance are plotted in Fig. 4(a) and (b). Unfortunately, the thermal damage of the electrode can be found when the welding voltage reaches 2.5 V. Although the peak load keeps improving with the increase of welding voltage, the thermal damage of the electrode also increases, and thus the maximum value under existing parameters can only become 102 N. The variation of joint resistance presents a trend of decreasing first and then increasing. The minimum value of weld resistance (1.05 m) is detected under 2.4 V, highlighted by a red circle in Fig. 4(b). However, the peak load under 2.4 V is 82.6 N (red circle in Fig. 4(a)), which is comparatively lower than expected. On the contrary, the advanced SSSW technique presents better welding quality. The mechanical property firstly increases slowly with the increase of welding voltage, and then severely reduces after reaching the maximum (see Fig. 4(c)). The variation trend of joint resistance is still similar to those of SDSW. The maximum peak load of SSSW joints is 119 N (2.2 and 2.3 V) and the minimum weld resistance is 0.88 m under 2.1 V (plots in Fig. 4(d)). That is to say, the advanced SSSW technique can obtain better interfacial bonding than the traditional SDSW technique with lower welding voltage and less thermal damage. Moreover, attributing to the thick filaments, the strength of Cu wires is improved. Therefore, the fracture region transfers from the wire–interconnector surface (type I, schematically displays in the inset of Fig. 4(a)) to the Kovar interconnector (type II, schematically displays in the inset of Fig. 4(c)) while the peak load is above 80 N. 3.1.2 Characterization of Welded Joints Under Typical Parameters In particular, specimens under 2.4 V of SDSW and 2.1 V of SSSW are emphasized in the present study. Figure 5 shows the cross-section macrostructure of joints under these parameters. It can be seen that compared with the SSSW (Fig. 5(b)), the distribution of Cu filaments in the SDSW joint is dispersed (Fig. 5(a)). Severe deformation and partial melting of Cu filaments near the wire-electrode #1 surface caused by welding heat can be detected in both joints. Particularly, significant melting of Ag coating on the surface of Cu filaments appears in the SSSW joint, and the mixed Ag effectively fills the gap between filaments. Besides, more filaments of the wire are joined with the interconnector in the SSSW joint, indicating the effective connection region at the wire-interconnector interface in the SSSW joint is larger than in the SDSW joint. Subsequently, the microstructure of both joints is further investigated. Figure 6 illustrates the features around the wire – interconnector interface. It can be observed that in

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Fig. 4. Tensile testing results and weld resistance of the thick wire-interconnector joints: (a) peak load of joints by SDSW, (b) weld resistance by SDSW, (c) peak load of joints by SSSW, and (d) weld resistance of joints by SSSW.

Fig. 5. Macrostructure of joints welded by different techniques under typical welding voltage: (a) fabricated by SDSW with a welding voltage of 2.4 V, and (b) joined by SSSW with a welding voltage of 2.1 V.

both joints, no obvious defects can be found at the wire-interconnector interface, while several voids are detected in the melted Ag (Fig. 6(a1) and (a1’)). Based on the EBSD results, newly formed grains by the melted Ag coating can be detected in both joints, as plotted in Fig. 6(b1) and (b1’), reflecting the total fusion bond. It is interesting to point out that the amount of Cu diffusion in the SSSW joint is larger than in the SDSW joint,

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Fig. 6. Microstructure and element distribution around the wire–interconnector interface: (a1) SEM image, (b1) IPF map, and (c1), (d1) EDS map of SDSW joint; (a1’) SEM image, (b1’) IPF map, and (c1’), (d1’) EDS map of SSSW joint.

in which the epitaxial growth of Cu grain and dendrite-like microstructure form in the melted Ag. Figure 7 shows the microstructure and element distribution around the interface between two Cu filaments adjacent to the wire-electrode #1 interface. The dendritelike microstructure is also found in the SDSW joint attributing to a more diffused Cu element, while a large Cu element enrichment zone in the melted Ag of the SSSW joint. Although some voids stay in the melted Ag, the formation of dendrite-like microstructure and Cu enrichment area is anticipated to help achieve reliable connections.

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Fig. 7. Microstructure and element distribution around the interface between two Cu filaments adjacent to the wire–electrode #1 interface: (a2) SEM image, (b2) IPF map, and (c2), (d2) EDS maps of SDSW joint; (a2’) SEM image, (b2’) IPF map, and (c2’), (d2’) EDS maps of SSSW joint.

3.2 Numerical Simulation of Welding Processes Therefore, studying the thermal and electrical behavior during the welding process is significant to reveal the relationship between the property and electrode system, as well as illustrate the mechanism of microstructure evolution. Figure 8 presents temperature and current density distribution in the typical cross-section of Cu wire at the time of reaching peak temperature during the SDSW process. As shown in Fig. 8(a), the high-temperature region is generated around the wire–electrode interface and the peak temperature of the entire joint is 1044 °C below the electrodes. The temperature gradually decreases from the wire–electrode interface to the wire–interconnector interface. It is worth noting that the peak temperature is mainly distributed in the peripheral regions of the wire, where excessive penetration is prone to occur during the welding process under higher welding voltages. Although the temperature at the wire–interconnector interface under this welding voltage is above the Ag-Cu eutectic temperature, the solid solution is preferentially formed due to the insufficient Cu element content. On the contrary, the Ag-Cu eutectic is deemed to form in the interface between Cu filaments adjacent to the wire– electrode interface for its higher temperature which accelerates the element diffusion. Simultaneously, the current density among the cross-section is shown in Fig. 8(b). It is clear that the peak current density (2485 A/mm2 ) forms adjacent to the wire–electrode interface, while the current density near the wire–interconnector interface is much lower. The current density distribution reveals an “electrode-wire-electrode” welding current path. In other words, heat generated at the wire-electrode interface is much larger than that at the wire–interconnector interface during the welding process. While heat prefers to generate at the wire-electrode interface, the temperature at the wire–interconnector interface is mainly demonstrated by thermal conduction. It is plausible to assume that thermal damage of wire and electrode caused by the over-heated interface will limit the application of the SDSW technique in joining more thick wires.

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Fig. 8. (a) Temperature and (b) current density distributions of the weld cross-section when the weld reaches peak temperature during the SDSW.

The temperature and current density distribution in a typical cross-section of wire at the time of reaching peak temperature during the SSSW process are plotted in Fig. 9. Comparing to the temperature distribution in the SDSW joint, a similar phenomenon can be observed in this joint that the highest temperature of Cu wire (1147 °C, above the melting point of Cu (1083 °C)) still stays at the wire-electrode interface, which corresponds to the melting of Cu wire in Fig. 5(b). However, the high-temperature region is uniformly distributed at the wire–electrode interface, which is available to prevent the unexpected severe deformation of Cu wire. The temperature at the wire–interconnector interface is far beyond the Ag-Cu eutectic temperature. Higher temperature promotes Cu atoms to diffuse in the melted Ag and form Ag-Cu eutectic, which presents as the dendric microstructure. Besides, the uniform distribution of current density among the cross-section of wire shows that only a few of the welding current passes along the wire. Instead, the welding current path presents an “electrode #1-wire-interconnectorMo plate-electrode #2” method. That is to say, the contact resistance between the wire and interconnector also takes part in the heat generation process. The temperature at the wire-interconnector interface is dominated by generated heat and thermal conduction. Higher interface temperature helps to form the eutectic microstructure and thus increases the effective connection area. Figure 10 exhibits the temperature curves at wire-electrode interface and wireinterconnector interface during the welding process of both welding techniques. The temperature increases rapidly in the voltage loading stage, whereafter gradually increases during the welding period and eventually reaches the peak temperature in the voltage unloading period. The heat generated during the welding process is given by Eq. 1, Q=

U2 T R

(1)

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Fig. 9. (a) Temperature and (b) current density distributions of the weld cross-section when the weld reaches peak temperature during the SSSW process.

Fig. 10. Temperature histories at the center of wire–electrode and wire–interconnector interfaces of (a) SDSW and (b) SSSW joints.

where Q is the heat generated (J), U is the welding voltage (V), R is the resistance (), and T is the duration of voltage application (s). Understandably, in the welding voltage loading stage, the temperature increases rapidly with the increase of welding voltage. In general, the increase in temperature can reduce the contact resistance but increase the bulk resistance. During the welding voltage loading stage, the temperature change significantly with the increase of welding voltage. In the duration of welding time, the increased bulk resistance prevents significant heat generation in the welding circuit. Owing to the comparatively high temperature of the entire joint after the welding stage, the heat generation is greater than the heat dissipation at the beginning of the voltage

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unloading stage and ultimately reaches the peak temperature. The temperature histories indicate a ~ 200 °C gap in the peak temperature between the two interfaces of the SDSW joint as well as a gap of ~ 160 °C in the SSSW joint. In summary, the heat generation in both interfaces leads to the melting of Ag coating and promotes the diffusion of Cu. The melted Ag flows downward and fills the gap by gravity, resulting in the formation of eutectics or solid solution with the dispersive distributed Cu. A larger bonded area, bigger tendency to form new grains, and less gap between filaments improve the peak load and reduce the electrical resistance by rebuilding the current path.

4 Conclusions In this study, the traditional SDSW and advanced SSSW techniques are conducted to join the Cu wire and Kovar interconnector with various welding voltages. The welding quality, microstructure features, and element distribution are investigated accordingly. Also, the thermal and electrical behavior during the welding process is revealed by numerical simulation. Based on the obtained results, the main conclusions are summarized as follows: 1) Mechanical and electrical properties present opposite variation tendency with the increase of welding voltage, and the fracture mode transfer from surface fracture into Kovar failure. Over-heated wire – electrode interface and under-heated wireinterconnector interface can be detected in the traditional SDSW joint, which limits its application in joining thick wires. 2) The peak temperature during the welding process can be detected at the interface of the electrode and Cu wire in both welding techniques, whereas the advanced SSSW technique can effectively reduce the temperature difference between the Cu wireelectrode interface and Cu wire-interconnector interface by adjusting the welding current path. 3) The thermal generation during the welding process promotes the melting and mixture of Ag coating, and accelerates the Cu element diffusion. The formation of AgCu eutectic or solid solution is beneficial to obtain better mechanical and electrical properties. Acknowledgement. The authors gratefully acknowledge financial support from National Natural Science Foundation of China (NSFC, Funding No. U1937601), and Science and Technology Commission of Shanghai Municipality (STCSM, Shanghai Pujiang Program, Funding No. 21PJ1405000).

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19. Fendrock, J.J., Hong, L.M.: Parallel-gap welding to very-thin metallization for high temperature microelectronic interconnects. IEEE Trans. Compon. Hybrids Manufact. Technol. 13, 376–382 (1990). https://doi.org/10.1109/33.56171 20. An, R., Xu, D., Wang, C.: Parallel-gap resistance welding between gold-plated silver interconnects and silver electrodes in germanium solar cells. In: 2014 15th International Conference on Electronic Packaging Technology, pp. 985–988 (2014). https://doi.org/10.1109/ICEPT. 2014.6922812 21. Ding, Y., et al.: Effect of energy input on mechanical and thermal fatigue property of parallel gap resistance welded joint between Si planar diode and Ag interconnector. In: 2022 23rd International Conference on Electronic Packaging Technology (ICEPT), pp. 1–6 (2022). https:// doi.org/10.1109/ICEPT56209.2022.9873196 22. Wang, S., Zhang, H., Li, Y., Zhang, W., Hang, C., Tian, Y.: Transient solid-liquid interfacial reaction between Al wire and Au/Cu pad during parallel gap micro-resistance welding. Mater. Lett. 288, 129340 (2021). https://doi.org/10.1016/j.matlet.2021.129340 23. Ding, Y., et al.: Joining mechanism of parallel gap resistance welded dissimilar connection between Ag interconnector and GaAs solar cell: A transmission electron microscopy study. Mater Charact 195, 112538 (2023). https://doi.org/10.1016/j.matchar.2022.112538 24. Zhan, X., Zhang, Q., Zhu, Z., Wei, Y.: Numerical simulation of resistance welding of solar cell using a thermal-electrical-mechanical coupled model. J. Mech. Sci. Technol. 32, 269–276 (2018). https://doi.org/10.1007/s12206-017-1227-5 25. Wu, Z., Yu, H., Su, B., Liu, S., Lin, J., Cui, X.: Develop solderability and welding heat source of the kovar/ag interconnector based on finite element simulation. In: 2020 21st International Conference on Electronic Packaging Technology (ICEPT), pp. 1–3 (2020). https://doi.org/ 10.1109/ICEPT50128.2020.9202859 26. Zeng, J., Cao, B., Tian, R.: Heat generation and transfer in micro resistance spot welding of enameled wire to pad. J. Manuf. Process. 82, 113–123 (2022). https://doi.org/10.1016/j.jma pro.2022.07.046 27. Zhang, H., Wang, S., Wu, B., Zhang, W., Hang, C., Tian, Y.: Ultrafast parallel micro-gap resistance Welding of an AuNi9 microwire and Au microlayer. Micromachines. 12, 51 (2021). https://doi.org/10.3390/mi12010051 28. Ding, Y., et al.: Effect of pre-welding and welding voltage on thermal fatigue property of parallel gap resistance welded joint between Ag interconnector and Au/Ag back electrode of GaAs solar cell. J. Manuf. Process. 92, 384–396 (2023). https://doi.org/10.1016/j.jmapro. 2023.03.012

Forming Characteristics of Additive Manufacturing Process by Rotating Arc Wenhang Li(B) , Lin Lu, Qinglin Han, Jiayou Wang, Jianxin Wang, Rui Yu, Feng Yang, and Jie Zhu Jiangsu University of Science and Technology, Zhangjiagang, China [email protected]

Abstract. In order to consider both the forming rate and quality of arc additive manufacturing, The rotating arc was applied to additive manufacturing in the paper and the experiments were conducted on it. Firstly, the effect of process parameters on the surface forming quality of single deposition bead was investigated and obtained the well-formed process ranges. On this basis, the effect of the rotating frequency on the forming characteristics of the single-layer multi-bead additive deposition layer was investigated. The deposited layers were uniform in width and no obvious defects, and as the rotating frequency increased, the surface flatness of the deposited layers became worse, the area of the heat affected zone between beads decreased, and the penetration increased. When the rotating frequency was 5 Hz, the surface flatness and penetration of the deposited layer were only 30% and 49% of the conventional non-rotating arc additive method. The results showed that the increase of arc rotating frequency inhibited the lateral heat conduction inside the deposited layer and prevented the molten metal from spreading to the previous deposition bead, which is an important reason for the change of deposited layer forming characteristics. The validation experiment showed that the rotating arc additive manufacturing method with the rotating frequency of 5 Hz can effectively improve the forming quality and forming characteristics of formed component. Keywords: Arc additive manufacturing · Rotating arc · Surface flatness · Forming characteristics

1 Introduction With the advantages of low cost, high deposition rate, and the ability to manufacture large metal structure parts in a short time, gas metal arc additive manufacturing has been applied in aerospace, automotive, defense, mold and die, and nuclear industries [1, 2]. However, its deposition layer still suffers from the problems of poor surface flatness, poor fusion effect and large penetration. This not only causes problems such as flow of molten metal and collapse of formed parts, but also increases the cost of subsequent finishing processes [3, 4]. Research has shown that the surface flatness and penetration could be improved and reduced by adjusting the deposition beads lap strategy through theoretical modeling © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. Chen et al. (Eds.): RWIA 2022, TIWM, pp. 56–69, 2024. https://doi.org/10.1007/978-981-99-9629-2_4

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[4–7] and optimizing the process parameters [8], but its regulation ability was limited. The main reason is that the conventional method has a small area of arc action, resulting in poor spreading of the molten metal. In addition, the arc always acts on the center of the deposition bead, resulting in larger heat input and arc pressure on the center of the deposition bead, so the penetration is larger. To sum up, the core issue is how to improve the distribution state of arc. Wang et al. improved the spreading of the deposition bead and thus the surface flatness by introducing a longitudinal magnetic field to expand the arc, but poor control of the excitation current would result in porosity and lack of fusion [9]. Han et al. used the twin electrode gas tungsten arc additive manufacturing to reduce the arc pressure to reduce the penetration of the formed part [10], but frequent replacement of tungsten electrode is required due to the rapid tungsten electrode loss and the problem of double tungsten electrodes touching each other affects the forming stability. The rotating arc is often applied to improve sidewall lack of fusion and eliminate finger type penetration in narrow-gap welding [11, 12]. In this paper, the method of rotating arc was used to improve the arc distribution state. The action positions of rotating arc and conventional non-rotating arc are shown in Fig. 1. As the rotating arc increases the action area of the arc, the arc will act directly on the previous deposition bead and its toe, which helps to improve the fusion effect between deposition beads. At the same time, the stirring effect of arc rotation on the molten pool will push the molten metal to spread and reduce the surface tension, which helps to increase its lap capacity. In addition, the arc rotation avoids the problem of large penetration caused by the conventional arc always acting on the center of the deposition bead.

Deposition bead

Arc D

D G

E C

A

A

B

B F

Penetration (a) Conventional non-rotating arc

E C

Remelting zone (b) Rotating arc

Fig. 1. Schematic diagram of the action position of rotating arc and conventional non-rotating arc

Therefore, in the paper, the rotating arc was applied to additive manufacturing and experiments were conducted to investigate the forming characteristics of the deposited layer at different rotating frequencies.

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The rest of the paper is organized as follows. In Sect. 2, the rotating arc additive manufacturing system and method were introduced. In Sect. 3, the effects of process parameters on the surface forming quality of single deposition bead were investigated. In Sect. 4, the forming characteristics of the deposition layers were investigated. In Sect. 5, the feasibility of fabricating metal components with rotating arc additive manufacturing was validated. In Sect. 6, conclusions were drawn.

2 Experimental System and Methods The rotating arc additive manufacturing system consists of a Panasonic YD-500KR power source, the wire feeder, the gas metal rotating arc welding torch [11], rotating arc controller and a high-speed camera. The schematic diagram of the system is shown in Fig. 2. In this work, a 1.2-mm diameter H08Mn2Si steel wire was employed as filling material deposited on a 300*150*10-mm Q345 steel substrate. The chemical composition of the wire and the substrate are shown in Table 1. The shielding gas was 80% Ar + 20% CO2 and the gas flow was controlled at 15 L/min. The eccentricity of conductive nozzle is 1.5 mm, and the wire extension is 15 mm.

Wire Hollow motor

Driver Rotating arc Photoelectric controller switch

Grating disk Carbon brush

Wire feeder

Eccentric conductive nozzle



V

High-speed camera

Power source



Substrate

Fig. 2. Schematic diagram of rotating arc additive manufacturing system

Firstly, the single deposition bead experiment was conducted by using the control variable method. The paper focuses on the effects of rotating frequency, wire feed speed, voltage and travel speed on the surface forming quality of the deposition beads. The considered values and ranges of deposition parameters are listed in Table 2. The deposition current is automatically matched to the wire feed speed by the power source.

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Based on the results of the single deposition bead experiment, the well-formed wire feeding speed, voltage and travel speed were selected and only the rotating frequency was changed to investigate the effect of rotating frequency on the inter-bead fusion, surface flatness and penetration of the deposited layer. According to the maximum allowable wire feed speed, the multi-layer multi-bead stepped components were conducted by the conventional arc additive method and rotating arc additive method respectively, and the quality of the formed components of the two methods was compared and analyzed. Table 1. Chemical composition of wire and substrate (wt. %) Elements

C

Si

Mn

P

S

H08Mn2Si

≤0.11

0.65 ~ 0.95

1.70 ~ 2.10

≤0.030

≤0.03

Q345

≤0.20

≤0.55

≤1.70

≤0.035

≤0.035

Table 2. Experimental condition of rotating arc additive manufacturing Parameter

Values of ranges

Rotating frequency (Hz)

0, 5 ~ 25

Wire feed speed (m/min)

3 ~ 10

Voltage (V)

21 ~ 26

Travel speed (mm/min)

60 ~ 300

3 Surface Forming Quality of Single Deposition Bead In the context of additive manufacturing, the forming appearance of the deposition beads is a crucial factor that influences the forming characteristics of deposition layer. Accordingly, it is necessary to investigate the surface forming quality of the beads with rotating arc additive manufacturing method under varied parameters. 3.1 Effect of Rotating Frequency on Surface Forming Quality The arc rotating frequency is one of the most important process parameters in rotating arc additive manufacturing. In this section, conventional arc and rotating arc with rotating frequencies of 5, 10, 15, 20 and 25 Hz were used for single deposition forming experiments. Other parameters were set as follows: wire feed speed 5m/min, travel speed 120 mm/min. The surface appearance of the deposition beads at different rotating frequencies was shown in Fig. 3. When the rotating frequency did not exceed 15Hz, the surface of each deposition bead was well-formed. Compared with the conventional non-rotating

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arc method, the surface of the rotating arc deposition bead showed a uniform fish scale pattern and was more perfect. However, when the rotating frequency exceeded 15Hz, large particle spatters appeared around the deposition bead. When the rotating frequency reached 25Hz, the number of large particle spatters increased sharply. This is because the droplets are subjected to centrifugal force when the arc is rotating, and the centrifugal force increases rapidly with the increase of the rotating frequency. As shown in Fig. 4, when the rotating frequency reached 25 Hz, the droplet flew away from the wire under the excessive centrifugal force and then fell around the deposition bead to form the large droplet spatter. The large particle spatters not only deteriorated the deposition bead forming quality, but also reduced the stability of the deposition process. In summary, the rotating frequency should be selected in a range of no more than 15 Hz.

Fig. 3. The surface appearance of the deposition beads at different rotating frequencies

Fig. 4. The large particle spatter formation process

3.2 Effect of Wire Feed Speed on Surface Forming Quality In order to ensure forming efficiency and avoid problems such as poor forming caused by too small wire feed speed, the value of wire feed speed starts from 3 m/min and gradually increases until the deposition bead cannot be well-formed. Other parameters were set as follows: rotating frequency 5 Hz, travel speed 180 mm/min. The surface appearance of the deposition beads at different wire feed speeds was shown in Fig. 5. It can be seen that when the wire feed speed was between 3 and 7 mm/min, the surface forming quality of the deposition bead was well-formed. When

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the wire feed speed exceeded 7m/min, the surface of the deposition bead was rough and oxidation occurs seriously. In addition, due to the high deposition current at this time, the heat input of the deposition process was too large and the solidification time of the molten metal increased. If this parameter is used for additive manufacturing, not only will the surface quality of the formed component deteriorate, but also the flow of molten metal or even the collapse of the formed component will easily occur during the additive process. In summary, the selection range of wire feed speed should be between 3–7 mm/min.

Fig. 5. The surface appearance of the deposition beads at different wire feed speeds

3.3 Effect of Voltage on Surface Forming Quality In the rotating arc additive manufacturing, the change of voltage directly affects the heat input and arc length of the arc to the deposition bead, and has an important influence on the forming quality and forming size of the deposition bead. In the actual additive manufacturing process, the selection of voltage needs to be matched with the wire feed speed. Therefore, in this section, the influence of voltage on the formation quality of rotating arc method was studied first, and the reasonable matching relationship between voltage and wire feed speed was concluded after a lot of experiments. The surface appearance of the deposition beads at different voltages was shown in Fig. 6. It can be seen that when the voltage was less than 23 V, the spreading ability of the deposition bead was poor. If this parameter is used for additive manufacturing, it is easy to produce lack of fusion at the lap of the deposition bead. When the voltage was 23 V and 24 V, the deposition bead was well-formed and has good spreading ability. When the voltage is higher than 24 V, the deposition bead was excessive spreading and the forming stability was reduced. Therefore, in order to obtain a well-formed deposition bead, the voltage needs to be reasonably matched with the wire feed speed. After extensive tests, the matching relationship between voltage and wire feed speed was obtained as follow: U = 17.3 + 1.45vf − 0.05vf2

(1)

In the following experiments, the voltage and the wire feed speed are matched according to Eq. (1).

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Fig. 6. The surface appearance of the deposition beads at different voltages

3.4 Effect of Travel Speed on Surface Forming Quality In arc additive manufacturing, the change of travel speed will not only affect the heat input, but also affect the amount of metal deposited per unit length, especially the forming size of the deposition bead. In order to study the influence of travel speed on the forming of single deposition bead, the selection range of travel speed was 60–300 mm/min. Other experiments parameters were set as follows: rotating frequency 10 Hz, wire feed speed 5 m/min. The surface appearance of the deposition beads at different travel speeds was shown in Fig. 7. It can be seen that when the travel speed was less than 120 mm/min, the wetting angle of the deposition bead was too large, which would cause poor fusion and stress concentration at the lap of the deposition beads. It is unfavorable to the forming quality of the formed components. When the travel speed was in the range of 120–300 mm/min, the deposition bead is well-formed and has good spreading ability.

Fig. 7. The surface appearance of the deposition beads at different travel speeds

4 Forming Characteristics of Single-Layer Multi-Bead Deposition Three sets of rotating frequencies of 5 Hz, 10 Hz and 15 Hz were selected for the experiments, and the single-layer components with 6 overlapping beads were deposited by the rotating arc additive manufacturing method. The experiments with conventional non-rotating arc additive manufacturing method (0 Hz) were also deposited as the control group. The center distance of adjacent deposition beads was 0.667 times the width of the deposited bead [7] to ensure the deposited layer forming flatness of the conventional

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arc additive method. As recommended by the process range proposed in this paper, the following deposition parameters were set: 6 m/min wire feed speed, 24V voltage, and 120 mm/min travel speed. After deposition, a 10-mm thick cross-section was cut in the middle of the deposited layer in the direction perpendicular to the travel direction. After grinding, polishing and etching, it was observed and analyzed with a Quartzoom5 3D video microscope. Photoshop software was used to measure each forming parameter. 4.1 Effect of Rotating Frequency on Inter-Bead Fusion Figures 8 and 9 show the macroscopic morphology of the deposited layers surface and cross section at different rotating frequencies, respectively. The deposition process was stable and the deposited layers were well-formed and uniform in width. The fusion between the deposited beads at different rotating frequencies was shown in Fig. 10. It can be seen that when the normal non-rotating arc deposition was used, there were lack of fusion at the toe, see Fig. 10(a). When the rotating arc deposition was used, the laps were well fused, see Fig. 10(b), (c), (d). This is because when non-rotating arc deposition was used, the wire was fed from the center hole of the common torch and the arc was located in the center of the current deposition bead. As shown in Fig. 11(a), the arc did not act directly on the previous deposition bead during the whole process, so the previous deposition bead received less heat, resulting in poor fusion, especially at the toe, which produced lack of fusion. When rotating arc deposition was used, due to the use of an eccentric conductive nozzle, the arc rotated synchronously with the inclined wire and periodically acted directly on the previous deposition bead and its toe, as shown in Fig. 11(b), when the

Fig. 8. Macroscopic morphology of the surface of the deposited layers

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previous deposition bead received more heat to make it fuse better and effectively avoided lack of fusion at the toe.

Fig. 9. Macroscopic morphology of cross section of deposited layers

Fig. 10. Effect of rotating frequency on fusion between beads and heat affected zone area

4.2 Effect of Rotating Frequency on Surface Flatness According to the literature [13], the surface flatness is reflected by the percentage of the average depth of the gaps on the top surface (h) and the average of beads height (h), and the smaller the value of h/h, the better the surface flatness. Figure 12 showed the variation of h/h with the rotating frequency. It can be seen that when the arc was not rotating, h/h was 0.144, and the surface flatness was the worst. When the rotating frequency was 5 Hz, h/h was 0.043, which was only 30% of that without rotating, and the surface flatness was the best. As the rotating frequency continued to increase to 15 Hz, h/h gradually increased to 0.095, and the surface flatness gradually became worse.

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This is because when the rotating arc was used for deposition, on the one hand the area of thermal action of the arc was increased, the previous deposition bead was heated more and the molten pool boundary was extended, promoting the spread of the molten metal to the previous deposition bead. On the other hand, the stirring effect of the rotating arc drove the spread of the molten metal to the previous deposition bead. These factors led to a higher lap position on the previous deposition bead after the molten metal had cooled, resulting in shallower gap and better surface flatness.

Fig. 11. Comparison of arc action positions. (a) position of arc action at 0 Hz; (b) the arc rotating to the position of the previous deposition bead at 5 Hz

Fig. 12. Effect of rotating frequency on h/h

The change of rotating frequency basically does not affect the overall heat input, but will affect the heat conduction inside the deposition layer. With the increase of rotating frequency, the thermal action time of the arc on the previous deposition bead was gradually shortened, resulting in a gradual decrease of the heating temperature of the previous deposition bead. Therefore, the transverse temperature gradient was gradually reduced, the transverse heat conduction was inhibited. And the molten pool boundary was retracted, the surface tension prevented the molten metal from spreading in the direction of the previous deposition bead during the deposition process. As a consequence, the

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lap position of the molten metal on the previous deposition bead was gradually reduced, and the surface flatness gradually deteriorated. The average area (S) of the heat affected zone between beads was used to reflect the effect of transverse heat conduction, and the variation of S with the rotating frequency was shown in Fig. 13. When the rotating frequency increased from 5 Hz to 15 Hz, S gradually decreased from 16.50 mm2 to 14.36 mm2 , indicating that the transverse heat conduction of the deposited layer was inhibited. This verified the results of the previous analysis.

Fig. 13. Effect of rotating frequency on average area of heat affected zone between beads

4.3 Effect of Rotating Frequency on Penetration Figure 14 showed the average penetration (P) of each deposition layer under different rotating frequencies. It can be seen that when the arc was not rotating, P was 2.08mm, and the penetration was the largest. When the rotating frequency was 5 Hz, P was 1.02mm, which was only 49% of that without rotating, and the penetration was the smallest. As the rotating frequency continues to increase to 15 Hz, P gradually increased to 2.07mm,

Fig. 14. Effect of rotating frequency on average penetration

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which tended to be the penetration without rotating. This is because the increase of rotating frequency will inhibit the transverse heat conduction inside the deposited layer, resulting in more heat conduction along the longitudinal direction, and therefore the penetration gradually increases.

5 Forming Quality of Multi-layer Multi-bead Components In order to consider both the forming rate and quality, the maximum allowable wire feed speed of 7 m/min was used, and the conventional non-rotating arc additive method and the rotating arc additive method with a rotating frequency of 5 Hz were used for the multi-layer multi-bead stepped components additive experiments. Other parameters were set as follows: 180 mm/min travel speed, 25 V voltage, and the length of each layer of the stepped components was 20 mm, 15 mm, 10 mm and 5 mm in order. The surface appearance of the multi-layer multi-bead stepped components were shown in Fig. 15. The stepped component made by conventional arc additive had collapsed and the surface was rough, and the forming quality was poor. The stepped component made by rotating arc additive was well-formed and had a flat surface. The variation of h/h for each layer of the stepped components was shown in Fig. 16. It can be seen that the surface flatness of each layer of the stepped component made by rotating arc additive was better than that of the stepped component made by conventional arc additive. The range of h/h with the conventional arc additive method was 0.38, while the range of h/h with the rotating arc additive method was only 0.14. The variation of width for each layer of stepped components is shown in Fig. 17. It can be seen that with the increase of the number of layers, the width of the stepped component with the conventional arc additive method increased rapidly, and the range of width was 4.80 mm. However, the width of the stepped component with the rotating arc additive method increased slowly, and the range of width was only 1.86 mm. In summary, compared with the conventional arc additive manufacturing method, the rotating arc additive manufacturing method with the rotating frequency of 5 Hz can effectively improve the forming quality and forming characteristics of formed component.

Fig. 15. The surface appearance of the multi-layer multi-bead stepped components

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Fig. 16. Variation of  h/h of each layer of the stepped components

Fig. 17. Variation of the width of each layer of the stepped components

6 Conclusions The rotating arc was applied to additive manufacturing in the paper and the experiments were conducted on it. The results of the experiments were analyzed and the conclusions were drawn as follow: (1) The range of each process parameter for well-formed deposition bead was obtained. The rotating frequency should not exceed 15 Hz, the wire feeding speed should be between 3–7 m/min, the travel speed should be between 120–300 mm/min. (2) The rotating arc can avoid lack of fusion at the toe between the deposited beads. (3) As the arc rotating frequency increases, the transverse heat conduction is inhibited, the heat affected zone between the beads is reduced, the surface flatness of the deposited layer gradually deteriorates, and the penetration gradually increases. (4) With 5 Hz rotating frequency, the surface flatness of the deposited layer is the best, only 30% of that without rotating, and the penetration is the shallowest, only 49% of that without rotating. (5) The experiments on stepped component validated that rotating arc additive manufacturing with the rotating frequency of 5 Hz can effectively improve the forming quality and forming characteristics of the formed component.

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References 1. Rodrigues, T.A., Duarte, V., Miranda, R.M., et al.: Current status and perspectives on wire and arc additive manufacturing (WAAM). Materials 12(7), 1121 (2019) 2. Singh, S.R., Khanna, P.: Wire arc additive manufacturing (WAAM): a new process to shape engineering materials. Mater. Today Proc. 44, 118–128 (2021) 3. Xia, C., Pan, Z., Polden, J., et al.: Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. J. Intell. Manuf. 33(5), 1467–1482 (2022) 4. Yaseer, A., Chen, H.: Machine learning based layer roughness modeling in robotic additive manufacturing. J. Manuf. Processes 70, 543–552 (2021) 5. Kumar, P., Jain, N.K., Sawant, M.S.: Development of theoretical models for dimensions of single-layer multi-track and multi-layer multi-track depositions by µ-PTA additive manufacturing process. J. Market. Res. 17, 95–110 (2022) 6. Chen, C., He, H., Zhou, J., et al.: A profile transformation based recursive multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM). J. Manuf. Process. 84, 886–901 (2022) 7. Xiong, J., Zhang, G., Gao, H., et al.: Modeling of bead section profile and overlapping beads with experimental validation for robotic GMAW-based rapid manufacturing. Robot. Comput.-Integr. Manuf. 29(2), 417–423 (2013) 8. Karadeniz, E., Ozsarac, U., Yildiz, C.: The effect of process parameters on penetration in gas metal arc welding processes. Mater. Des. 28(2), 649–656 (2007) 9. Wang, Q., Zhu, S., Xu, S., et al.: Influence of longitudinal magnetic field on surface accuracy of aluminum alloy arc melting forming. Ordnance Mater. Sci. Eng. 42(4), 11–14 (2019) 10. Han, Q., Li, X., Zhang, G.: Dual-wire dual-tungsten-argon arc additive manufacturing of low carbon steel/high strength steel composite structures. Trans. China Welding Inst. 43(2) (2022) 11. Wang, J., Guo, H., Yang, F.: New high speed rotating arc narrow gap MAG welding. Trans. China Welding Inst. 2005(10), 65–67+4 (2022) 12. Sankar N, Malarvizhi S, Balasubramanian V, et al. Effect of Rotating Arc (Spin Arc) on Mechanical Properties and Microstructural Characteristics of Gas Metal Arc Welded Armour Steel Joints[J]. Transactions of the Indian Institute of Metals, 2022: 1–13 13. Han, Q.L., Li, X., Dong, M., et al.: Enhanced curve-fitting model of the bead section profile and the corresponding overlapping model for twin-electrode gas tungsten arc–based additive manufacturing. Int. J. Adv. Manuf. Technol. J. Adv. Manuf. Technol. 116(3), 1151–1167 (2021)

A Fast Point Cloud Reconstruction Algorithm for Saddle-Shaped Weld Seams in Boiler Header Joints Yang Lu, Huabin Chen(B) , Mingzhen Rao, and Kai Liu School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China [email protected]

Abstract. In boiler industries, the automated welding of header and tube-seat joints is a critical topic. Traditional offline programming method suffers from deviations between models and real workpieces. Vision aided welding provides a feasible way to deal with the problem, but the point cloud acquisition and reconstruction are yet to be developed. In this paper, a fast point cloud reconstruction method for saddle-shape welding seams in boiler header and tube-seat joints has been proposed. The point clouds of header and tube-seat joints in boilers are acquired by a partition scanning strategy, and then reconstructed using a two-step registration method. The partial point clouds are first coarsely spliced using position relationship, and then fine registered to improve accuracy. Besides, a simple but effective benchmark, the registration mark, has been proposed. The performance of splicing in each stage has been evaluated with the registration mark. The registration mark after fine registration in each iteration is lower than 1 mm. Finally, the partial clouds are spliced to form a complete cloud. Keywords: Saddle-shaped weld seam · Point cloud splicing · 3-D Reconstruction

1 Introduction Boiler is one of the most critical components in thermal energy generation industries. In boiler plants, working substance such as boiler water or vapor is transferred through vessels, and those vessels are attached to collecting headers, where those substance is gathered and redistributed evenly to the whole system. Since tube-seat size varies drastically over different components, it is of much difficulty to realize automated welding for those joints between headers and tube-seats. The main methods for welding such joints recently can be concluded as manual TIG welding and automatic SAW welding by specially designed welding machines. Researches on robotic welding in such situation have been focused on offline programming [1, 2], which determines welding trajectories or plans welding process before actual welding. Such method depends totally on the given models of workpieces to be processed. However, in real manufacturing environments those workpieces have various sizes and machining errors. Besides, stresses created © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. Chen et al. (Eds.): RWIA 2022, TIWM, pp. 70–81, 2024. https://doi.org/10.1007/978-981-99-9629-2_5

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during welding will also bring deformation to the workpieces. Those deviations make offline programming less robust and causes failure in welding automation. Vision system brings a reasonable solution to such dilemma by acquiring real-time data of the workpieces so that the deviations between real workpieces and models can be determined and compensated. Point cloud is one of the most commonly used representation of three-dimensional information, because of its simplicity and acceptable accuracy [3–5]. Researches have been conducted on point cloud aided welding, in which point cloud of workpieces are acquired and weld planning or trajectory extraction have been developed based on those point clouds. Yang et al. [6] have proposed a weld seam extraction and path planning method based on point cloud segmentation. The point clouds of workpieces are first acquired by depth camera. Then the point clouds are segmented and weld seams have been extracted based on these segmented planes. The method, however, is mostly suitable for planar workpieces. Gómez-Espinosa et al. [7] acquire the colored point cloud of workpiece, and use the color information to extract the weld seam by setting color threshold. Such method is quite novel, but its robustness under disturbance and effectiveness under complex situations are not yet proved. Jia et al. [8] adopt the linear laser to acquire point cloud of workpiece, and then the weld seam feature points are extracted. The extracted points are transformed to robot coordinate system and used in real welding experiments. Gao et al. [9] reconstruct the point cloud of intersecting pipes, and then extract the weld seam from the point clouds using a normal-based algorithm. The accuracy of the method is verified to be fine. However, in their method they focus only on the horizontal pipe and get rid of the intersecting vertical pipe, so that the point cloud can be acquired directly from above. In real cases, however, the intersecting pipes are installed in advance, and shading is inevitable. The point cloud for header and tube-seat joints is more difficult to acquire. Problems come that the welding seams and grooves have a saddle-shaped spatial structure, and the shading from intersecting pipes make it impossible to acquire the whole point cloud of workpieces from a single shot. Therefore, the point clouds of different part of the same workpiece shall be acquired from different viewpoints. Then these should be reconstructed, in other words, spliced into a complete point cloud of the whole workpiece. Typically, the point cloud splicing work is called point cloud registration, and can be divided into two steps: coarse registration and fine registration [10]. The overall target of point cloud registration is to find a transformation matrix to minimize difference between two point clouds. In this paper, a novel point cloud reconstruction algorithm has been proposed for those saddle-shaped weld seams in header and tube-seat joints. The method consists of a fast and simple coarse registration process based on point cloud transformation and a fine registration processing using iterative closest point (ICP) based algorithms. The point cloud reconstruction algorithm has both high efficiency and accuracy. The follow content is organized as follows: In Sect. 2, the experimental setup will be introduced. In Sect. 3, methods for point cloud acquisition and reconstruction and experimental results are performed. Finally, in Sect. 4, a conclusion is provided of the whole paper.

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2 Experimental Set-Up 2.1 Vision System Set-Up The vision system for point cloud acquisition and reconstruction mainly consists of a robot manipulator and its control cabinet, a binocular camera, a host machine and the point cloud processing software developed on host machine. Robot of FANAC M10i series is chosen according to the requirement of robot reachability and joint motions. A Chishine Tracer P1 camera is used in the system because of its small size and acceptable accuracy. The camera is installed in an eye-in-hand way in order to cope with the special imaging angles. That is, the camera is attached to the robot manipulator by a specially designed clamp. In such way, the eye-hand relationship is fixed once the camera is installed. The 3D camera has a repeat precision of 0.1 mm and a resolution of 960*600, and is protected with a specially designed metal shelter against dust and arc during welding. The host machine is a laptop with a Intel i7-6700HQ CPU inside and 8 g memory, and point cloud processing software is developed on it. The whole system is connected with ethernet with a switch to form a local area network, so that communication between camera, robot and host machine can be achieved. The whole experimental platform is shown in Fig. 1.

Fig. 1. Vision system set-up

The size of workpiece sample and its installation platform is as shown in Fig. 2. The main pipe has a diameter of 190.7 mm and the branch pipe has a diameter of 141.3 mm. Its installation platform is specially designed to cope with the intersecting pipe structure of the workpiece and enable its moving.

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Fig. 2. (a) Size of workpiece; (b) size of cart and workpiece; (c) workpiece and installation platform

2.2 Image Acquiring Since the complete point cloud cannot be acquired in a single shot, a partitioned image taking strategy is proposed. To be specific, the workpiece is photographed from different viewpoints and partial point clouds are acquired. Then the partial clouds are spliced to reconstruct the complete cloud of workpiece. Besides, point cloud registration requires overlapping point cloud, while larger overlapping area ratio leads to more partial clouds to be acquired. To achieve a balance in such conflict, the angle between two adjacent

Fig. 3. (a) The illustration of photographing positions; (b) 1.Front abdomen; 2.Right side; 3.Back abdomen; 4. Left side

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photographing positions is nearly 90°, and therefore the overlapping ration is around 50% for two adjacent clouds. The photographing positions are shown in Fig. 3. Notice that the machined groove surface has strong reflection to both visible and invisible light, therefore in order to acquire point cloud with good quality and less noise, special coating shall be sprayed on the surface to reduce reflection. In the paper, photographic developer is spray onto the workpiece surface, which coats the machined surface with a thin layer of white particles. The point clouds acquired at different viewpoints are shown in Fig. 4.

Fig. 4. Raw clouds acquired at (a) front abdomen; (b) right side; (c) back abdomen; (d) left side

3 Point Cloud Reconstruction After acquiring partial point clouds from different photographing positions, the point cloud should be spliced to form the complete point cloud of the workpiece. Theoretically, the process of point cloud splicing is to find the homogeneous transformation between two point clouds so that the spatial distance between the similar regions of the two clouds is minimized. Generally, the point cloud to be transformed is called source cloud, and is annotated as P = {p1 , p2 , p3 , . . . , pi , . . . , pn }. The other cloud is called target cloud and is annotated as Q = q1 , q2 , q3 , . . . , qj , . . . , qm . By applying the homogeneous transformation to source cloud, it is rotated and translated, and the target of point cloud splicing is to optimize the error function in Eq. (1). f =

1 K

K  i=1

||Tpi − qi ||

(1)

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where K represents the size of similar region point cloud to be taken into calculation, and T is the homogeneous transformation matrix:   R3×3 t3×1 T= (2) 0 1 where R represents the rotation transformation matrix and t represents the rigid translation vector, both in 3D space. In this paper, the flowchart of point cloud reconstruction process is shown in Fig. 5. The first input point cloud is set as the base of reconstruction, and then the others are spliced to it one after another. The former source cloud is transformed and fused to the target cloud, and then the fused cloud serves as the target cloud for the next iteration.

Fig. 5. Flowchart of point cloud reconstruction

3.1 Point Cloud Coarse Registration Theoretically, according to the coordinate system transformation chain shown in Fig. 6, the homogeneous transformation can be derived. Supposing that the workpiece is photographed from two distinct camera positions with an overlapping region, and two pieces of point cloud are acquired, noticed as P1 and P2 . Points representing the same region of workpiece on P1 and P2 are distinct under camera coordinate system, and should be the

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same after applying point cloud splicing. Therefore, the equations can be established as Eq. (3): ⎧W B t c ⎨ P=W B T · t T1 · c T · P1 W P = W T · BT · t T · cP (3) 2 t 2 c B ⎩ cP = T · cP 1 2 where W P refers to coordinates of the point on workpiece under world coordinate system, and c P2 represents the cloud of overlapping region in P1 and P2 , respectively, W B T is the homogeneous transformation from robot base coordinate system to world coordinate system, Bt T1 and Bt T2 are the transformation from TCP coordinate system to robot base coordinate system, and tc T is the transformation from camera coordinate system to tool coordinate system, i.e., the eye-hand matrix as the result of eye-hand calibration.

cP 1

Fig. 6. Coordinate system transformation chain

By combining the equations in (3), the point cloud splicing matrix can be derived as: T=

t −1 B −1 B · t T1 · t T2 · tc T . cT

(4)

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Therefore, the partial point clouds could have been spliced once the eye-hand matrix is acquired and the positions and poses of the two points are read from robot. In practice, however, it is found that the point cloud splicing matrix calculated from Eq. (4) still cause much error to splicing work because of errors from robot, TCP calibration and eye-hand calibration. The direct splicing result is shown in Fig. 7.

Fig. 7. Deviations caused by direct splicing (circled in red)

Though not accurate, those positions are close enough for fine point cloud registrations. Therefore, the transformed point clouds shown in Fig. 7 are taken as the coarse registration result. Compared with traditional coarse registration algorithms such as 4Points Congruent Sets (4PCS) [11], Fast Point Feature Histogram (FPFH) [12] or Normal Distribution Transform (NDT) [13, 14], the method is simple and fast, and its result is close enough for fine registration algorithms. 3.2 Point Cloud Fine Registration Given a good initial position, Iterative Closest Point (ICP)-based fine registration algorithm is chosen as the fine registration method, because of its simplicity and efficiency. Both classic ICP algorithm and the trimmed ICP algorithm is implemented using Point Cloud Library (PCL). The basic idea of ICP algorithm includes two main steps, correspondence determination and transformation estimation [15]. Since the correspondences are unknown, the algorithm adopted an iterative way to generally converge to the optimization. The trimmer ICP (Tr-ICP) algorithm is quite similar to ICP, except that it uses a least guaranteed overlapping ratio ξ. The basic idea is to get rid of those non-overlapping points when optimizing the loss function [16]. To be specific, the points having larger distance to its correspondence will be trimmed. In such way, the partially overlapping point clouds can be registered more efficiently and effectively. It is worthy noticing that when ξ tends to 1, Tr-ICP will be exactly the same as ICP. With regard to the partially overlapping characteristic of partial point clouds, and given the prior knowledge that the point clouds have an overlapping ratio around 0.5 or less, the least overlapping ratio ξ for Tr-ICP algorithm is set to be 0.4 for the former two registration process and 0.6 for the last registration process. Also, to judge the registration accuracy, an easy but reasonable criterion, the registration mark, is designed. The mark η is easily calculated by finding the nearest point in target cloud to each point in source cloud. Then the Euclidean distance between them

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is calculated and sorted in ascending order. According to the preset overlapping ratio ξ, the top ξ elements are preserved and their average is calculated as the registration mark. Pseudocode for calculating registration marks is demonstrated in Algorithm 1. This criterion is calculated under the assumption that each point in source is closest to its corresponding point in target.

Algorithm 1: Registration Mark Calculation Input: overlapping ratio ξ , transformed source cloud P ′ , target cloud Q Output: registration mark % Create a list to save the closest point distance values list % Initializing registration mark variable =0 = ( , ) do for = 0, 1, … , ( ) = ( , ) = . ( ) end % Sort the distance list in ascending order ( ) aesc ′ ⌋ =⌊ for = 0,1, … , ′ do 1 = + ′ end

Both ICP and Tr-ICP method have been performed, and their performance has been recorded in Table. The point cloud fine registration process is as shown in Fig. 8 (a) and (b) (Table 1). Table 1. Performance of ICP and Tr-ICP

ICP

Tr-ICP

progress

Input size

Overlapping ratio

Time consumed

Registration mark

2/4

3033

1

95.375 s

0.6022 mm

3/4

3840

1

73.052 s

1.1824 mm

4/4

3162

1

11.26 s

0.2232 mm

2/4

3033

0.4

14.258 s

0.6484 mm

3/4

3840

0.4

4.049 s

1.0365 mm

4/4

3162

0.6

2.321 s

0.1761 mm

It is obvious that Tr-ICP algorithm is much faster than ICP algorithm, while at the same time having equivalent or even better registration accuracy. Therefore, the cloud spliced by Tr-ICP is finally adopted as the output from cloud splicing process.

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Fig. 8. Registration process. (a) source (red) and target (blue) before fine registration; (b) source (red), target (blue) and registration result (green); (c) point cloud after fusing

After each registration process, the registration mark is calculated as in Table 2. The registration mark is tested to be reasonable, since better registration results lead to lower registration marks. To examine the performance of proposed method, registration mark is taken as the benchmark, and the registration mark before coarse registration, after coarse registration and after fine registration have been summarized in Table 2. Table 2. Registration mark at each stage

Before registration

After coarse registration

After fine registration (Tr-ICP)

Progress

Registration mark (mm)

2/4

>1000

3/4

>1000

4/4

>1000

2/4

2.2941

3/4

5.4163

4/4

2.4765

2/4

0.6484

3/4

1.0365

4/4

0.1761

It is found that the registration mark after coarse registration is quite close, while after fine registration the result is quite accurate with an average distance around or smaller than 1 mm between overlapping regions of transformed source cloud and target cloud. Therefore, the method is reasonable and effective.

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3.3 Point Cloud Fusing Since the partial point clouds should be reconstructed into a complete point cloud model of workpiece, the source cloud and the target cloud shall be fused into a single cloud. The fused cloud, according to the flowchart above, serves as the target cloud for next iteration. The fusing work simply adds points from transformed cloud to target cloud, and voxel grid filtering is applied to average the point cloud density distribution. The voxel size is set to be 5 mm*5 mm*5 mm, and the point cloud fusing result after each iteration is shown in Fig. 8(c). Finally, the point cloud of workpiece after fusing and voxel filtering has been shown in Fig. 9.

Fig. 9. The fused point cloud (a) before voxel grid filtering; (b) after voxel grid filtering

4 Conclusions In this paper, the point clouds of header and tube-seat joints in boilers are acquired and reconstructed. To deal with the difficulty in acquiring complete point cloud of workpiece, a partition scanning strategy is adopted, which acquire partial point clouds from different photograph positions. After that, point cloud reconstruction has been realized by a twostep method. The partial point clouds are first coarsely spliced based on their position relationship and rigid transformation. Then fine registration is conducted to improve splicing accuracy. A simple but effective benchmark has also been approved, that is the registration mark. The performance of different registration methods has been compared, and Tr-ICP is chosen to be better method. The registration mark after fine registration in each iteration is lower than 1 mm. Finally, the partial clouds are spliced one after another into a complete cloud of workpiece. The method performs well both in efficiency and accuracy. Future work can be focused on extracting features from the reconstructed cloud to instruct welding.

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References 1. Shi, L., Tian, X., Zhang, C.: Automatic programming for industrial robot to weld intersecting pipes. Int. J. Adv. Manuf. Technol. 81, 2099–2107 (2015) 2. Hong, L., Wang, B., Xu, Z., et al.: Research on off-line programming method of spatial intersection curve welding based on VTK. Int. J. Adv. Manuf. Technol. 106, 1587–1599 (2020) 3. Aldoma, A., Marton, Z.C., Tombari, F., et al.: Tutorial: point cloud library: three-dimensional object recognition and 6 DoF pose estimation. IEEE Robot. Autom. Mag. 19(3), 80–91 (2012) 4. Kobbelt, L., Botsch, M.: A survey of point-based techniques in computer graphics. Comput. Graph. 28(6), 801–814 (2004) 5. Alexa, M., Gross, M., Pauly, M., et al.: Point-based computer graphics. ACM SIGGRAPH 2004 Course Notes. 2004: 7-es. 6. Yang, L., Liu, Y., Peng, J., et al.: A novel system for off-line 3D seam extraction and path planning based on point cloud segmentation for arc welding robot. Robot. Comput.-Integr. Manuf. 64, 101929 (2020) 7. Gómez-Espinosa, A., Rodríguez-Suárez, J.B., Cuan-Urquizo, E., et al.: Colored 3D path extraction based on depth-RGB sensor for welding robot trajectory generation. Automation 2(4), 252–265 (2021) 8. Jia, Z., Wang, T., He, J., et al.: Real-time spatial intersecting seam tracking based on laser vision stereo sensor. Measurement 149, 106987 (2020) 9. Gao, J., Li, F., Zhang, C., He, W., He, J., Chen, X.: A method of D-Type weld seam extraction based on point clouds. IEEE Access 9, 65401–65410 (2021). https://doi.org/10.1109/ACC ESS.2021.3076006 10. Gu, X., Wang, X., Guo, Y.: A review of research on point cloud registration methods. IOP Conf. Ser. Mater. Sci. Eng., 022070 (2020) 11. Aiger, D., Mitra, N.J., Cohen-Or, D.: 4-points congruent sets for robust pairwise surface registration. ACM SIGGRAPH 2008 papers, 1–10 (2008) 12. Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3212–3217 (2009) 13. Magnusson, M., Lilienthal, A., Duckett, T.: Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robot. 24(10), 803–827 (2007). https://doi.org/10.1002/rob.20204 14. Biber, P., Straßer, W.: The normal distributions transform: a new approach to laser scan matching. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453), pp. 2743–2748 (2003) 15. Besl, P.J., Mckay, N.D.: Method for registration of 3-D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, pp. 586–606 (1992) 16. Chetverikov, D., Svirko, D., Stepanov, D., et al.: The trimmed iterative closest point algorithm. In: 2002 International Conference on Pattern Recognition, pp. 545–548 (2002)

Feature Extraction and Classification Recognition of Molten Pool in Multi-layer and Multi-pass Welding of Medium and Thick Plates Zhanying Xue1 , Hao Zhou1 , Runquan Xiao1 , Zhen Hou1 , Erbin Liu2 , Guobao Tang2 , and Shanben Chen1(B) 1 Intelligentized Robotic Welding Technology Lab, School of Materials Science and

Engineering, Shanghai Jiao Tong University, Shanghai 200240, China [email protected] 2 Guangzhou Risong Intelligent Technology Holding Co. Ltd., Guangzhou, China Abstract. Multi-Layer and Multi-Pass Welding (MLMPW) for medium thick plates is widely used in fields such as marine engineering, nuclear welding, and high-speed rail welding. The implementation of feature extraction and classification of different layer weld pools in multi-layer and multi pass welding of medium and thick plates can accurately distinguish the types of weld pools in different layers during the welding process, whether the current weld pool meets normal weld pool parameters, and is of great significance for path planning, real-time correction, and penetration prediction control of multi-layer and multi pass welding. This article is based on the welding experiment of multi-layer and multi-pass welding of medium and thick plates, and carries out image processing and feature extraction of the weld pool in multi-layer and multi-pass welding of medium and thick plates based on visual information and current and voltage information. The classification of the weld pool in four layers and seven passes is also studied. This article uses a U-net network-based fusion pool image segmentation method to segment and process the fusion pool image, and provides relevant evaluation indicators to objectively evaluate the segmentation effect of the U-net fusion pool image. The edge of the segmented fusion pool image is extracted, and the definition of the maximum width of the fusion pool and the curvature radius of the three parts is given, and relevant parameters are calculated, and classify the four layers and seven weld seams according to the calculated pool parameters and provide the corresponding classification parameter range. Keywords: Robot welding · multi-layer and multi-pass welding · weld pool image processing · weld pool classification · U-net network

1 Introduction In recent years, with the continuous increase in labor costs and the progress of science and technology, robots have become a trend in intelligent manufacturing to replace manual welding operations in complex environments. Welding operations are gradually developing towards intelligence and automation. The welding environment is often © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. Chen et al. (Eds.): RWIA 2022, TIWM, pp. 82–106, 2024. https://doi.org/10.1007/978-981-99-9629-2_6

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accompanied by splashing, strong arc light, and gas interference. Using robots instead of manual labor during the welding process can liberate workers from harmful environments and improve welding efficiency. Chen proposed the concept of intelligent welding manufacturing and proposed applying intelligent technology to the welding process in complex environments to achieve intelligent control of welding dynamic processes [1]. He et al. proposed a BNM that extracts relevant information through visual sensors to achieve autonomous decision-making of gas metal arc multi-pass welding positions for T-joints in automated manufacturing. The results show that it can effectively improve manufacturing efficiency and automation level [2]. In the welding operation process, achieving automatic path planning is the key point of the welding process. Who proposed a method for extracting weld contour feature points in the article, which can achieve automatic multi-pass path planning and guide the initial welding position of each layer in the MAG arc welding process. The experimental results show that this method is feasible [3]. In thick plate welding, because the change of groove size and assembly gap will affect the accuracy of multi pass path planning and welding quality, Yang et al. designed a set of passive vision sensors and established a double sided double arc welding system, which can effectively realize the multi pass path planning of thick plate double sided Arc welding. [4]. The application fields of medium and thick plate are very extensive, mainly in fields such as high-pressure vessel production, marine engineering, automobile production, logistics and transportation Due to the large size and complex structure of medium and thick plate structural components, in most cases, multi-layer and multi-pass welding is used for welding and filling of medium and thick plate structural components[5]. Before multi-layer and multi pass welding of medium and thick plates, the initial point positioning of the weld seam is generally carried out through manual teaching, and then the weld seams of different layers are filled with welding through manual preaching [6]. In the actual welding process of medium and thick plates, it is often encountered that single pass welding is difficult to ensure welding quality, such as wider welds and thicker plates. Therefore, multi-layer and multi-pass welding is often used for the welding of medium and thick plates. The welding method of multi-layer and multi-pass welding has the advantages of low heat input, reduced welding deformation, and reduced probability of defect generation. It is very common to apply the welding method of multi-layer and multi-pass welding to the field of medium and thick plate welding [7]. To ensure the welding quality of medium and thick plates, multi-layer and multi pass welding is usually carried out in the form of groove opening. Due to the existence of a certain groove angle, different layers of fusion welding beads have different layers. In the robot medium and thick plate multi-layer and multi pass welding process, it is first necessary to pre plan the number of welding layers filled in the weld seam, the number of welding beads required for each layer, and the corresponding welding parameters. The welding filling sequence of different layers of weld seams needs to be planned in advance [8]. For multi-layer and multi-pass welding, due to its low level of intelligence, robot multi-layer and multi-pass welding usually adopts online teaching methods. Automatic planning of welding paths is one of the key technologies to achieve intelligent welding of medium and thick plate robots. Zhang Huajun et al. conducted research on the path planning of typical single-sided V-shaped and double-sided K-shaped groove multi-pass welding,

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In the article, the sequence of single sided V-shaped or U-shaped groove weld beads is arranged as shown in Fig. 1, and the customized multi-pass welding path planning strategy is mainly derived. The customized path planning for single sided V-shaped groove multi-pass S-shaped welds is carried out, ultimately achieving the simulation of robot multi-pass welding and rapid generation of motion programs, improving teaching flexibility and efficiency [9].

Fig. 1. Weld bead arrangement sequence

Sensors, as a detection device for obtaining information, can convert a measured physical quantity into another physical quantity that is convenient for transmission and processing. They are commonly used in automatic control and measurement systems. This chapter mainly discusses the practical application of sensors in the welding process. The key to achieving automation and intelligence in the welding process lies in providing accurate real-time welding dynamic process information for the control system, enabling it to adjust welding parameters within a certain range according to different welding conditions to ensure the stability of the welding dynamic process [10]. The dynamic welding process contains rich information, which can be collected through corresponding sensing information collection systems. The information of these welding processes includes visual information of the molten pool, current and voltage information, arc sound information, etc., which respectively reflect the characteristics of different welding dynamic processes [11]. Nowadays, building a sensing information collection platform that integrates multiple information sensing systems to achieve realtime collection and processing of multi-sensor information during the welding process has become the key to achieving dynamic welding process control. Zhang Zhifen and others from Shanghai Jiao Tong University have built a pulse GTAW welding test system and a multi information collection system based on spectral information sensing, sound information sensing, voltage sensing, and visual sensing technology, The schematic diagram of the multi information collection system is shown in Fig. 2. The experimental platform and collection system have achieved automatic collection and storage of welding arc spectrum, sound, arc voltage, and weld seam image information. With the help of multi-source synchronous information, automatic control of the welding process has been ultimately achieved [12]. The weld pool image contains rich information related to welding quality. The collected weld pool images can be processed using specific algorithms to calculate the relevant parameters of the weld pool, such as weld length information, weld width information, weld curvature radius information, etc. By establishing a certain model between these weld pool related information and welding quality, online prediction and control of welding forming quality can be achieved. Due to strong interference such as arc light

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Fig. 2. Schematic diagram of multi information acquisition system

and splashes during the welding process, the obtained molten pool image contains a large amount of interference information, which makes it difficult to process and extract features of the molten pool image. The traditional process of molten pool image processing generally includes preprocessing operations for molten pool images, segmentation of molten pool images, feature extraction of molten pool, and output of relevant feature parameters. Due to the fact that the input molten pool image is a grayscale image containing a large amount of noise, the preprocessing operations for molten pool generally include filtering, binarization, and morphological processing, among which filtering processing also includes common median filtering processing Gaussian filter processing is used to reduce the noise of the input gray image of the molten pool, so as to reduce interference. The binary processing of a molten pool image refers to the process of classifying and assigning values to pixels in the image. By setting the grayscale value of the pixels on the image to 0 or 255, the entire molten pool image is presented with a clear effect of only black and white. Due to the fact that the region edges of the binary image obtained after thresholding the molten pool image are not very ideal, corrosion or expansion processing can be selected according to the actual situation to perform “shrinkage” or “expansion” operations on the region. As the two most basic and important morphological operations, corrosion and expansion processing are the foundation of many advanced morphological processing, Corrosion treatment and expansion treatment can be combined to derive other morphological algorithms. Figure 3 shows the flowchart of traditional melt pool image processing. In recent years, many scholars at home and abroad have integrated computer technology and artificial intelligence technology into the real-time collection of weld pool images, using relevant weld pool image processing algorithms to extract feature information of the weld pool, in order to analyze the internal relationship between weld pool

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Fig. 3. Flow chart of traditional molten pool image processing

features and welding quality [13], and prepare for achieving real-time control of welding quality and obtaining high-quality weld seam formation. Nowadays, many scholars have systematically defined the geometric parameters of the positive molten pool, including the width of the molten pool, the length of the molten pool, the back angle of the molten pool, and the area of the molten pool. By exploring the relationship between the geometric parameters of the molten pool and the welding parameters, real-time adjustment of relevant parameters is achieved to control the welding process and achieve good welding quality [14]. Figure 4 is a schematic diagram of the front geometric feature parameters of the molten pool.

Fig. 4. Schematic Diagram of Geometric Characteristic Parameters of the Front of the Bath

2 Fusion Pool Image Segmentation and Edge Extraction Based on U-Net Network U-net network is an image segmentation network based on Convolutional Neural Networks (CNN), proposed in 2015. When this network was first proposed, it was used for cell image segmentation, that is, to segment cell walls. Later, it has been widely used in lung node detection and retinal blood vessel extraction. Due to the good performance of the U-net segmentation method in the medical field and the significant similarities between molten pool images and cell images, this article attempts to transfer the U-net based segmentation method to the segmentation application of molten pool images. The following text will first introduce the U-net network, then discuss the process of molten pool image segmentation based on U-net, and finally display the segmentation results of molten pool images based on U-net, And evaluate the segmentation effect of U-net molten pool image through four evaluation indicators from four perspectives. Firstly, the

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U-net network is a typical Encoder Decoder structure, with the Encoder part responsible for extracting features and the Decoder part responsible for upsampling operations. U-net can rely on a small number of datasets for network model training, meeting the characteristics of multi-layer and multi-pass welding of medium and thick plates due to high experimental difficulty, large workload, and limited data sets of melt pools. Therefore, U-net used a large amount of data augmentation operations during the training phase, and achieved good results in previous practical applications. The network structure diagram of the U-net network is shown in Fig. 5. From the U-net network structure diagram, it can be seen that the left half of the network is the Encoder part. This part performs downsampling on the input and maximizes pooling to achieve downsampling operations. The right half of the network structure is the Decoder part, which upsampling the output of the Encoder and restoring resolution. The upsampling operation is achieved through Upsample, with skip connections in the middle for feature fusion, All convolutional layers in the entire network, except for the final output layer, are 3 * 3 convolutions. The entire network structure resembles a large “U” character, therefore, the network is called a U-net network. In the schematic diagram of the network structure, each blue box corresponds to a multi-channel feature map, with the number of channels scaled in the box. The gray box represents the copied and cropped feature maps, with arrows indicating different operations. For the purpose of this experimental research, obtaining a large amount of molten pool image data is relatively difficult, as the U-net network can achieve more effective use of limited annotated data by relying on data augmentation from very small training images. In addition to its unique characteristics in feature fusion, U-net networks also have many other advantages in image segmentation, including relying on five pooling layers to achieve multi-scale feature recognition of image features, and the upsampling part extracts and outputs multi-scale features through fusion feature extraction.

Fig. 5. U-net network structure diagram

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Before using the U-net-based fusion pool image segmentation method to segment the fusion pool image, it is necessary to prepare the relevant dataset for model training and testing. Firstly, the obtained fusion pool image is obtained through Python related programs to obtain the ROI area of the fusion pool image, and uniformly cropped to a size of 512 * 512. This is an important step in creating the dataset, which is produced using labelme software. Labelme can achieve many functions, including labeling images in the form of polygons, rectangles, circles, multiple segments, and points. It is mainly used for tasks such as object detection and image segmentation. Labelme labeling images in the form of flags can be used for image classification and cleaning tasks. In this experiment, a dataset of molten pool images required for U-net network training was created using labelme. The molten pool image folder was opened on the labelme interface, and create polygons were selected to annotate the molten pool image on a single sheet. The edges of the molten pool were delineated using a single point method and set as weldpool, while the rest was the background part. Simply put, the original molten pool image was divided into two parts: the molten pool part and the Beijing part, The segmentation of molten pool shapes through U-net is essentially a binary classification operation. The label image of the molten pool image obtained by labeling the molten pool image. The image of the molten pool annotated by labelme is shown in Fig. 6. Subsequently, visualize the label data, as shown in Fig. 7, and the label image of the melt pool is shown in Fig. 8. Due to the fact that the file format outputted after labeling the molten pool image through labelme is a Jason format file, which contains label information. As the Jason format file cannot be directly used as a dataset for U-net model training, a unified file conversion is needed to become a molten pool image dataset that can be used for network training and testing. The dataset of the melt pool specifically includes two parts. The first part is the label part of the dataset named label, which is the image part generated after labelme processing. The second part is the file name of the original image part of the melt pool corresponding to the Label data.

Fig. 6. Labelme labeled image

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Fig. 7. Visual display

Fig. 8. Image of molten pool label

After building the U-net network, it is also very important to set the hyperparameter of the network structure and select the optimizer. The optimizer is a tool to guide the neural network to update parameters. After the loss function is calculated, it needs to carry out backpropagation through the optimizer to complete the update of network parameters. Different optimizers differ mainly because they define different first order momentum and second order momentum. The first order momentum is a gradient related function, and the second order momentum is a gradient square related function, Common optimizers mainly include random gradient descent method, Momentum, AdaGrad, RMSProp, and Adam optimizers. In the network structure constructed in this article, RMSProp is selected as the optimizer to update network parameters. As an extension of AdaGrad, RMSProp performs better in non convex cases by adding second-order momentum to SGD, which can achieve good convergence and faster convergence when the objective function is unstable. The RMSProp optimizer can avoid the problem of rapid gradient

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reduction, has strong adaptive learning rate ability, and can achieve fast convergence in the case of unstable objective functions. It performs better than the random gradient descent method, Momentum, and AdaGrad optimizers. In the U-net network structure constructed in this article, the RMSProp optimizer is used to eliminate oscillations during gradient descent to accelerate gradient descent,. When updating weights, choose to use the method of dividing by the root sign to make larger gradients significantly smaller and smaller gradients slightly smaller, which can reduce the fluctuations in the direction of larger gradients. During the entire gradient descent process, the oscillation will be relatively small, and a larger learning rate can be set to increase the learning step, thus achieving the goal of accelerating the learning of the network structure. Next comes the training and testing section of the U-net network. In this section, the labels of the molten pool dataset processed by labelme are shown in Fig. 9. After building the U-net network, the network structure was trained. The overall dataset consisted of 194 molten pool images, and the molten pool dataset was divided in a 7:3 ratio. According to the division, a total of 144 datasets were used for network structure training, and 40 molten pool test set images were used for testing. The epoch during network training was set to 40, Batch_ Set the size to 1 and the learning rate Lr to 0.00001. After setting the relevant parameters, start training the U-net network. The segmentation effect is demonstrated through the segmentation results of the U-net network on the molten pool image test set, as shown in Fig. 9.

Fig. 9. Test set segmentation effect

In order to quantitatively evaluate the effectiveness of U-net in the segmentation of molten pool images, in this section, four rating indicators were evaluated based on the segmentation test results of U-net in the molten pool image dataset, including MLOU, mPA, mPrecision, Recall. The results obtained are shown in Fig. 10, Fig. 11, Fig. 12, and Fig. 13.

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Fig. 10. MLOU Result Chart

Fig. 11. mPA Result Chart

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Fig. 12. mPrecision Result Chart

Fig. 13. mRecall Result Chart

Next, randomly select one of the four layers and seven layers of molten pool obtained for actual segmentation effect display. Randomly select an original image of a molten pool with different layers and a molten pool image segmented by U-net network to display the segmentation effect, as shown in Fig. 14.

Feature Extraction and Classification Recognition of Molten Pool

Fig. 14. Images of different layers of molten pool segmented by U-net network

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Fig. 14. (continued)

3 Calculation of Characteristic Parameters of Molten Pool The width of the molten pool is shown in Fig. 15. The width data of the molten pool can be calculated by taking the bounding rectangle of the molten pool. The width of the molten pool can be obtained by subtracting the horizontal coordinate value of the pixels on the right side of the bounding rectangle from the horizontal coordinate value of the pixels on the left side of the bounding rectangle (Fig. 16).

Fig. 15. Schematic Diagram of Melting Pool Width

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Fig. 16. Schematic Diagram for Calculation of Actual Melt Pool Width

Fig. 17. Schematic diagram of the edge of the three parts of the molten pool to calculate the radius of curvature

In order to classify the weld pool images of different layers and explore the range of relevant parameters for different layers of weld pool under welding process parameters in this article, the curvature radius of the weld pool has been defined. In this article, the maximum values of the curvature radius of three parts were selected as the core of the exploration, namely the maximum value of the curvature radius of the upper part of

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Fig. 18. Fitting diagram of three part edge curve of molten pool

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the molten pool QU, the maximum value of the curvature radius of the right part of the molten pool QR, and the maximum value of the curvature radius of the lower part of the molten pool QD, as shown in Fig. 17. The maximum values of the curvature radius of the three parts of the molten pool were obtained by curve fitting the edge curves of the three parts of the molten pool, Extract the coordinate values of the three parts of the edge and draw a curve for curve fitting through cubic curve fitting. The curve fitting results of the three parts of the weld pool edge are shown in Fig. 18, which are the cubic curve fitting results of the upper part of the weld pool edge curve, the right part of the weld pool edge curve, and the lower part of the weld pool edge curve. Table 1 shows the parameter table of the cubic fitting function of the three parts of the edge curve. Table 1. Parameter table of cubic fitting function of three part edge curve curve

a

b

c

d

Upper part

3.21881634e−06

2.66159707e−03 −7.15807627e−01 5.42134240e+01

Right part

−3.52005962e−05 1.55047825e−02 −1.81631808e+00

7.38086474e+01

Lower part −3.84258629e−05 1.63441720e−02 −1.87622245e+00

6.63815740e+01

4 Classification of Typical Weld Pool Characteristics for Different Layers and Passes in Multi-layer and Multi-pass Welding of Medium and Thick Plates The previous section of the article discussed the processing of molten pool images and the extraction of related features through relevant processing methods. The features of molten pool mainly include the maximum curvature radius values of the molten pool width and three parts. This section will explore how to classify molten pools in different layers through the obtained relevant data. The previous section calculated the relevant data of different layers of molten pools. In this section, five molten pool images will be randomly selected for each layer and each weld pool, and their molten width and the maximum curvature radius of the defined three parts will be presented. Based on the displayed data, a range of molten width and curvature radius values of each layer and each weld pool will be defined. Firstly, the first layer of the backing weld pool is the first layer. Five randomly collected images of the backing weld pool are selected, and the edge of the pool is segmented to calculate the width of the pool, the maximum curvature radius of the three parts of the pool, and other related data. Due to the small current of the backing weld, the current is selected as 175A, and there is a limitation on the groove sidewall during the backing weld process. Therefore, the front end of the pool has a small sharp angle, The process of backing welding is shown in Fig. 19. Randomly analyze the characteristic parameters of the molten pool from five images of the first layer of molten pool. For the molten pool of backing welding, the classification criteria of the molten pool can be

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determined through the molten width data. The specific parameters are shown in Table 2. The fusion width data obtained from five randomly selected fusion pool images shows that the minimum fusion width is 105 and the maximum fusion width is 107. Therefore, under this process parameter, the fusion width range of backing welding is between 105 and 107. Therefore, we set the parameter range for determining the fusion pool of the first layer of backing welding, that is, under this process parameter, the fusion width of the normal backing welding fusion pool is 105 < W < 110.

Fig. 19. Schematic Diagram of Backing Welding

Table 2. Parameters of the first weld pool of the first layer 1c1d

W

QU

QR

QD

1c1d01

105

\

\

\

1c1d02

105

\

\

\

1c1d03

105

\

\

\

1c1d04

107

\

\

\

1c1d05

107

\

\

\

Next is the analysis of the second layer and second layer of molten pool. The welding process of the second layer and second layer is a groove with both sides of the workpiece, and the forming shape is trapezoidal. Randomly select five images of the second weld pool in the second layer and analyze their feature parameters, as shown in the table. It can be found that the minimum value of the melt width in the five images of the second layer and the second weld pool is 157, the maximum value of the melt width is 159, and the range of the melt width is between 157 and 159. Among the five melt pool images, the maximum value of the curvature radius in the upper part of the melt pool is 79, 96, and the maximum value of the curvature radius in the upper part is between 79 and 96, The maximum curvature radius of the right half is between 44 and 47, the maximum curvature radius of the right part is between 44 and 47, the maximum curvature radius of the lower part of the molten pool is between 102 and 108, and the maximum curvature radius of the right part is between 102 and 108. Therefore, under this process parameter condition, the characteristic parameters of the second layer of molten pool with good weld quality should meet the following requirements: 155 < W < 160; 75 < QU < 100; 45 < QR < 50; 100 < QD < 110 (Fig. 20 and Table 3). For the third layer and third weld pool, as the welding process of the third layer and third weld pool involves one side being the groove of the welding workpiece and one

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Fig. 20. Welding Diagram of the Second Weld on the Second Layer

Table 3. Parameters of the second weld pool of the second layer 2c2d

W

QU

QR

QD

2c2d01

157

79

47

104

2c2d02

157

83

45

113

2c2d03

158

89

45

108

2c2d04

159

89

46

104

2c2d05

159

96

44

102

side being the weld seam, the shape of the weld seam is formed in a parallelogram, as shown in Fig. 21. Five randomly selected images of the third layer and third weld pool are analyzed, and the relevant parameters of the randomly selected third layer and third weld pool are shown in Table 4. The five randomly selected images of the third layer and the third layer of the molten pool have a minimum width of 159 and a maximum width of 165, ranging from 155 to 165. Among the five molten pool images, the maximum curvature radius of the upper part of the molten pool is 85 and 103, and the range of the maximum curvature radius of the upper part is 85 to 103. The maximum curvature radius of the right part of the five molten pool images is 37 and 41, respectively, The range of maximum curvature radius in the right part is between 37 and 41. Among the randomly selected five molten pool images, the maximum curvature radius in the lower part of the molten pool is between 76 and 82, and the range of maximum curvature radius in the lower part of the molten pool is between 76 and 82. Therefore, under the conditions of this process parameter, the relevant characteristic parameters of the molten pool to obtain stable welding quality should meet the following requirements: 155 < W < 165; 80 < QU < 105; 35 < QR < 45; 75 < QD < 85.

Fig. 21. Welding Diagram of the Third Weld on the Third Layer

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Table 4. Schematic Diagram of the Third Weld on the Third Layer Welding Pool Parameters of the Third Weld on the Third Layer 3c3d

W

QU

QR

QD

3c3d01

159

99

38

79

3c3d02

159

103

37

77

3c3d03

161

85

41

79

3c3d04

161

88

40

82

3c3d05

163

92

40

76

For the third layer and fourth weld pool, as the third layer and fourth weld pool have one side as the welding workpiece groove and one side as the weld seam, the weld seam is formed in a trapezoidal shape, as shown in Fig. 22. Five images of the third layer and fourth weld pool are randomly selected for analysis. The relevant feature parameters of the five randomly selected weld pools of the third layer and fourth weld pool are shown in Table 5. The five randomly selected images of the third and fourth layers of the molten pool have a minimum width of 145 and a maximum width of 149, ranging from 145 to 149. Among the five molten pool images, the maximum curvature radius of the upper part of the molten pool is 83 and 101, and the range of the maximum curvature radius of the upper part is 83 to 101. The maximum curvature radius of the right part of the five molten pool images is 24 and 27, respectively, The range of maximum curvature radius in the right part is between 24 and 27. Among the randomly selected five molten pool images, the maximum curvature radius in the lower part of the molten pool is between 86 and 101, and the range of maximum curvature radius in the lower part of the molten pool is between 86 and 101. Therefore, under the conditions of this process parameter, the relevant characteristic parameters of the molten pool to obtain stable welding quality should meet the following requirements: 140 < W < 150; 80 < QU < 105; 85 < QR < 105; 85 < QD < 105.

Fig. 22. Welding Diagram of the Fourth Weld on the Third Layer

For the weld pool of the fourth and fifth layers, as the fourth and fifth layers have a welding workpiece groove on one side and a weld seam on the other side, the weld seam is formed in a parallelogram shape as shown in Fig. 23. Five images of the fourth and fifth layers of the weld pool are randomly selected for analysis, and the relevant feature parameters of the five randomly selected weld pools of the fourth and fifth layers are shown in Table 6. The five randomly selected molten pool images in the fourth and fifth layers have a minimum width of 151 and a maximum width of 155, ranging from 151

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Table 5. Parameters of the fourth weld pool of the third layer 3c4d

W

QU

QR

QD

3c4d01

145

97

24

101

3c4d02

145

101

27

107

3c4d03

147

90

26

99

3c4d04

147

83

27

93

3c4d05

149

86

27

86

to 155. Among the five molten pool images, the maximum curvature radius of the upper part of the molten pool is 102 and 148, and the range of the maximum curvature radius of the upper part is 102 to 148. The maximum curvature radius of the right part of the five molten pool images is 41 and 46, respectively, The range of maximum curvature radius in the right part is between 41 and 46. Among the randomly selected five molten pool images, the maximum curvature radius in the lower part of the molten pool is between 71 and 78, and the range of maximum curvature radius in the lower part of the molten pool is between 71 and 78. Therefore, under the conditions of this process parameter, the relevant characteristic parameters of the molten pool to obtain stable welding quality should meet the following requirements: 150 < W < 155; 100 < QU < 150; 40 < QR < 50; 70 < QD < 80.

Fig. 23. Welding Diagram of the Fifth Weld on the Fourth Layer

Table 6. Parameters of the fifth weld pool of the fourth layer 4c5d

W

QU

QR

QD

4c5d01

151

115

44

77

4c5d02

151

115

44

78

4c5d03

153

148

45

74

4c5d04

153

131

41

77

4c5d05

155

102

46

71

For the fourth and sixth weld pool, as both sides of the fourth and sixth weld are welded seams, the weld seam is formed in a parallelogram shape as shown in Fig. 24. Five images of the fourth and sixth weld pool are randomly selected for analysis, and

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the relevant feature parameters of the five randomly selected weld pools of the fourth and sixth layers are shown in Table 7. The five randomly selected images of the fourth and sixth layers of the molten pool have a minimum width of 151 and a maximum width of 155, ranging from 151 to 155. Among the five molten pool images, the maximum curvature radius of the upper part of the molten pool is 102 and 148, and the range of the maximum curvature radius of the upper part is 102 to 148. The maximum curvature radius of the right part of the five molten pool images is 41 and 46, respectively, The range of maximum curvature radius in the right part is between 41 and 46. Among the randomly selected five molten pool images, the maximum curvature radius in the lower part of the molten pool is between 71 and 78, and the range of maximum curvature radius in the lower part of the molten pool is between 71 and 78. Therefore, under the conditions of this process parameter, the relevant characteristic parameters of the molten pool to obtain stable welding quality should meet the following requirements: 150 < W < 155; 100 < QU < 150; 40 < QR < 50; 70 < QD < 80.

Fig. 24. Welding diagram of the sixth weld on the fourth layer

Table 7. Parameters of the sixth weld pool of the fourth layer 4c6d

W

QU

QR

QD

4c6d01

160

73

41

119

4c6d02

161

67

41

128

4c6d03

161

71

41

133

4c6d04

157

69

39

135

4c6d05

157

69

39

131

For the fourth and seventh weld pool, as both sides of the fourth and seventh weld are welded seams, the weld seam is formed in a parallelogram shape as shown in Fig. 4, 5, 6 and 7. Five images of the fourth and seventh weld pool are randomly selected for analysis, and the relevant feature parameters of the five randomly selected weld pools of the fourth and seventh layers are shown in Tables 4, 5, 6 and 7. The five randomly selected molten pool images in the fourth and seventh layers have a minimum width of 139 and a maximum width of 143, ranging from 139 to 143. Among the five molten pool images, the maximum curvature radius of the upper part of the molten pool is 53 and 64, and the range of the maximum curvature radius of the upper part is 53 to 64. The maximum curvature radius of the right part of the five molten pool images is 20 and 24, respectively, The range of maximum curvature radius in the right part is between 20

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and 24. Among the randomly selected five molten pool images, the maximum curvature radius in the lower part of the molten pool is between 54 and 63, and the range of maximum curvature radius in the lower part of the molten pool is between 54 and 63. Therefore, under the conditions of this process parameter, the relevant characteristic parameters of the molten pool to obtain stable welding quality should meet: 135 < W < 145; 50 < QU < 65; 20 < QR < 25; 50 < QD < 65 (Fig. 25 and Table 8).

Fig. 25. Welding Diagram of the Seventh Weld on the Fourth Layer

Table 8. Parameters of the seventh weld pool in the fourth layer 4c7d

W

QU

QR

QD

4c7d01

143

56

23

54

4c7d02

141

61

22

58

4c7d03

143

64

23

61

4c7d04

143

63

24

61

4c7d05

139

53

20

63

In summary, in this chapter, five molten pool images corresponding to the four layers and seven weld seams are randomly selected, and data such as the molten pool width and the maximum curvature radius of the three parts are obtained. Then, based on the range of characteristic parameters of the obtained data, the relevant parameter range of the molten pool corresponding to different weld seam shapes under the same process parameter conditions is determined. Based on the results of previous calculations, the conclusions obtained are as follows:, Under the welding process parameters set in the experiment, the parameter range corresponding to the normal welding quality of the molten pool is as follows: for the first layer of backing welding, the range of molten pool parameters is: the width of the molten pool is: 105 < W < 110; For the second weld seam of the second layer, the parameter range of the melt pool is: 155 < W < 160; 75 < QU < 100; 45 < QR < 50; 100 < QD < 110; For the third layer and third weld seam, the parameter range of the melt pool is: 155 < W < 165; 80 < QU < 105; 35 < QR < 45; 75 < QD < 85; For the third layer and fourth weld seam, the parameter range of the melt pool is: 140 < W < 150; 80 < QU < 105; 85 < QR < 105; 85 < QD < 105; For the fourth and fifth weld seam, the parameter range of the melt pool is: 150 < W < 155; 100 < QU < 150; 40 < QR < 50; 70 < QD < 80; For the fourth and sixth weld seam, the parameter range of the melt pool is: 150 < W < 155; 100 < QU < 150; 40 < QR < 50; 70 < QD < 80; For the fourth and sixth weld seam, the parameter range of

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the melt pool is: 135 < W < 145; 50 < QU < 65; 20 < QR < 25; 50 < QD < 65. By setting the parameter range of the four layer and seven pass weld pool, the foundation can be established for the monitoring of the weld pool and online control of the welding process of multi-layer and multi pass welding of medium and thick plates. Randomly select one molten pool image from the remaining first layer of the first backing weld, calculate the molten pool width and the maximum curvature radius of the three parts of the molten pool. After calculation, the molten pool width of the randomly selected one is 107, which is within the above classification parameter range; Randomly select one image from the remaining second layer and second pass of the molten pool, calculate the width of the molten pool, and the maximum curvature radius of the three parts of the molten pool. After calculation, the width of the randomly selected molten pool is 218, the maximum curvature radius of the upper part is 92, the maximum curvature radius of the right part is 46, and the maximum curvature radius of the lower part is 105, within the above classification parameter range; Randomly select one image from the remaining third layer and third layer of the molten pool image, calculate the width of the molten pool and the maximum curvature radius of the three parts of the molten pool. After calculation, the width of the randomly selected molten pool is 162, the maximum curvature radius of the upper part is 98, the maximum curvature radius of the right part is 44, and the maximum curvature radius of the lower part is 75, which is within the above classification parameter range; Randomly select one image from the remaining weld pool images of the third and fourth layers, calculate the width of the weld pool and the maximum curvature radius of the three parts of the weld pool. After calculation, the width of the randomly selected weld pool is 147, the maximum curvature radius of the upper part is 103, the maximum curvature half diameter of the right part is 23, and the maximum curvature radius of the lower part is 110, within the above classification parameter range; Randomly select one image from the remaining fourth and fifth layers of the molten pool image, calculate the width of the molten pool and the maximum curvature radius of the three parts of the molten pool. After calculation, the width of the randomly selected molten pool is 151, the maximum curvature radius of the upper part is 102, the maximum curvature radius of the right part is 44, and the maximum curvature radius of the lower part is 71, which is within the above classification parameter range; Randomly select one image from the remaining fourth and sixth layers of the molten pool image, calculate the width of the molten pool and the maximum curvature radius of the three parts of the molten pool. After calculation, the width of the randomly selected molten pool is 158, the maximum curvature radius of the upper part is 69, the maximum curvature radius of the right part is 38, and the maximum curvature radius of the lower part is 134, within the above classification parameter range; Randomly select one image from the remaining fourth and seventh layers of the molten pool image, calculate the width of the molten pool and the maximum curvature radius of the three parts of the molten pool. After calculation, the width of the randomly selected molten pool is 137, the maximum curvature radius of the upper part is 61, the maximum curvature radius of the right part is 20, and the maximum curvature radius of the lower part is 66, which is within the above classification parameter range.

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5 Conclusion This article proposes a representative seven types of typical weld pools corresponding to four layers and seven welds in multi-layer and multi-pass welding of medium and thick plates. The image segmentation method for the seven types of weld pools in multi-layer and multi-pass welding of medium and thick plates based on U-net network is used to segment the seven types of weld pools, and evaluation indicators are provided to evaluate the segmentation effect of the weld pool image. The parameter features of the seven types of weld pools are defined and extracted, And based on the parameter features, the classification of molten pools was achieved and corresponding feature parameter ranges were established, which prepared for real-time path planning for multi-layer and multi-pass welding of medium and thick plates, online adjustment of welding process parameters, control of welding gun posture, and prediction control of penetration depth. Acknowledgements. This work is partly supported by the National Natural Science Foundation of China under the Grant No. 61873164.

References 1. Chen, S.-B.: On intelligentized welding manufacturing. In: Tarn, T.-J., Chen, S.-B., Chen, X.Q. (eds.) Robotic Welding, Intelligence and Automation. AISC, vol. 363, pp. 3–34. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18997-0_1 2. He, Y., Ma, G., Chen, S.: Autonomous decision-making of welding position during multipass GMAW With T-joints: a Bayesian network approach. IEEE Trans. Industr. Electron. 69(4), 3909–4391 (2021) 3. He, Y.S., Xu, Y.L., Chen, Y.X., Chen, H.B., Chen, S.B.: Weld seam profile detection and feature point extraction for multi-pass route planning based on visual attention model. Robot Comput. Integr. Manuf. (2015). https://doi.org/10.1016/j.rcim.2015.04.005 4. Yang, C.D., Ye, Z., Chen, Y.X., Zhong, J.Y., Chen, S.B.: Multi-pass path planning for thick plate by DSAW based on vision sensor. Sens. Rev. 34, 416–23 (2014) 5. Fang, Y., Xun, X., Zhang, H., Sun, B., Zhan, X.: Research on optimization technology of multi layer and multi pass welding for medium and thick plate. Weld. Technol. 45(3), 5–897 (2016) 6. Jia, W.: Multilayer and multi-channel dynamic routing planning and weld positioning based on machine vision. Beijing Institute of Petrochemical Technology, Beijing (2022) 7. Cheng, L., Wang, T., Hou, Y., Zheng, J., Yang, Z.: Research on multi layer and multi pass robot welding technology for V-shaped groove of medium and heavy plate. Welding (02), 10–13+62 (2018) 8. Zhu, X.: Research on multi-layer and multi-channel automatic transverse welding process of medium and heavy plate robot. Tianjin University, Tianjin (2017) 9. Zhang, H., Zhang, G., Cai, C., Yin, Z., Wu, L.: Customized weld bead arrangement strategy for thick plate arc welding robots. J. Weld. 30(03), 61–64+116 (2009) 10. Chen, S., Lin, T., Chen, W., Qiu, T.: The concept and technology of intelligent welding manufacturing engineering. J. Weld. 25(6), 124–127 (2004) 11. Wu, D.: Research on VPPAW forming prediction and intelligent control of aluminum alloy based on multi-source information fusion. Shanghai Jiao Tong University, Shanghai (2018)

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12. Zhang, Z.: Research on feature extraction of welding defects in aluminum alloy pulse GTAW process based on multiple information fusion. Shanghai Jiao Tong University, Shanghai (2015) 13. Chen, S.B., Zhao, D.B., Wu, L., et al.: Intelligent methodology for sensing, modeling and control of pulsed GTAW part 2-Butt joint welding. Weld. J. 79(6), 164–174 (2000) 14. Wen, Q., He, J., Wang, A.: Research status and development trend of visual sensing technology for welding pool image. Hot Work 45(22), 7–10+15 (2016)

Adaptive Ant Colony Algorithm Based on Global Scanning Cui Can, Zhi Heng, Jiang Junnan, Tang Xiaoxiang, and Wang Xuewu(B) Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China [email protected]

Abstract. In recent years, with the widespread application of industrial robots, intelligent path planning algorithms have attracted much attention due to their advantages over traditional algorithms. Among them, the ant colony algorithm is studied due to its good robustness and faster convergence speed. For traditional ant colony algorithms, slow convergence speed and easily falling into local optima are two disadvantages. Hence, some strategies are introduced to improve its performance. They are distance factor direction selection rules, global scanning, adaptive weighting coefficients adjustment, and variable initial pheromone distribution. The strategies reduce the limitation of step size to some extent, and show significant advantage in grids with uneven obstacle distribution. By adopting the strategy of directional angles, the confusion in path finding caused by the increased reachable points is avoided. Experiments show that the algorithm has advantages of high efficiency and low complexity in solving large and complex maps, it greatly improves the speed of initial solutions construction and convergence. Keywords: ant colony algorithm · adaptive · global scanning · path planning

1 Introduction In recent years, the artificial intelligence industry is rising rapidly, large machine production is gradually replacing manual labor, and path planning for industrial robots has become a hot research topic. Path planning refers to planning the optimal or nearoptimal path in time and path under constraints such as safety and collision avoidance. Common path planning algorithms currently include artificial potential field method [1], RRT algorithm [2], A* algorithm [3] and intelligent optimization algorithms [4]. Among them, intelligent optimization algorithms are further divided into genetic algorithm [5], neural network [6], ant colony algorithm [7], particle swarm optimization algorithm [8], etc. The ant colony algorithm was first proposed by Italian scholars Colorni A., Dorigo M. in 1991 by observing the characteristics of ants foraging. It has attracted attention for its good robustness and superior global planning ability. However, the basic ant colony algorithm also has disadvantages such as easy to fall into local optimal solutions and slow initial convergence speed. Many scholars have proposed some improvements to the above deficiencies of the basic ant colony algorithm. Zhou Zhiping [9] proposed © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. Chen et al. (Eds.): RWIA 2022, TIWM, pp. 107–125, 2024. https://doi.org/10.1007/978-981-99-9629-2_7

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to improve the distance heuristic factor, dynamically adjust the weighting coefficient, improve the pheromone volatilization coefficient, and adjust the volatilization coefficient based on simulated annealing algorithm. Sun Chunzhe [10] proposed to increase the distance change heuristic factor, establish a double ant colony full crossover algorithm, incorporate the idea of maximum and minimum ant colony, and use population two to optimize the path of population one. Liu Tianfu [11] et al. proposed to add elite ants to expand the search scope, visual perspective, and remove unnecessary tortuous points. Khaled A [12] proposed the stimulation probability, heuristic information based on infinite step size, expanded vision, adopted a new pheromone update rule, and dynamically adjusted the volatilization coefficient. Ma Shixuan et al. [13] proposed an ant colony algorithm based on dynamic pheromone update and path reward and punishment. Ma Feiyu et al. [14] proposed a heterogeneous double population ant colony algorithm with global vision, which can well solve the inconvenience caused by fixed step size to ant addressing. Wang Ziyang et al. [15] proposed an adaptive ant colony algorithm. However, the improvements made to the basic ant colony algorithm so far do not have significant effects on the rapid convergence in the initial search and on maps with complex corridor obstacles. Based on the research of predecessors, this paper adaptively improves the weighting coefficients and distance heuristic factors, so that the weighting coefficients and distance heuristic factors can be adjusted in a timely manner according to the degree of ant search and the distance to the end point; introduces the stimulation probability, and improves its shortcomings to better adapt to this algorithm, and unevenly distributes initial pheromones according to the number of surrounding obstacles; adopts distance factor direction selection rules to improve the original single state transition rules; further optimizes the global scanning, and the feasible domain of ants is no longer limited to surrounding nodes; uses directional angle constraint functions to screen nodes with too many insignificant differences in the ant’s field of vision; finally introduces a fallback strategy to prevent ants from deadlock. The algorithm is simulated and tested in Matlab, and compared with classic and improved ant colony algorithms, with good feasibility.

2 Environment Modeling The commonly used two-dimensional environment modeling methods include geometric method, visual graph method, grid method, etc. Among them, the grid method has been widely used for its easy implementation, simple form, and intuitiveness. Up to now, many studies on improved algorithms are also based on the grid method. Therefore, this paper models the environment based on the grid method. As the name implies, the grid method divides the environment into r × r squares, numbering them orderly from left to right and top to bottom. Obstacle grids are represented by black grids, and obstacle-free grids are represented by white grids. The overall grid environment is shown in Fig. 1. The robot can only move within the white grids. In the two-dimensional plane, the robot can move in eight directions, as shown in Fig. 2. The robot’s movable distance has two types. The correspondence between numbering and coordinates is shown in formula (1):  xi = mod(i − 1, Nx ) + 0.5 (1) yi = int( i−1 Ny ) + 0.5

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where mod() and int() are built-in functions in Matlab, representing modulo and fix functions respectively. xi represents the x coordinate of the center point of the grid numbered i, and yi represents the y coordinate of the center point of the grid numbered i.

Fig. 1. Environmental modeling

Fig. 2. Right direction robot can move

3 Basic Ant Colony Algorithm 3.1 Principle of Ant Colony Algorithm The basic ant colony algorithm is inspired by the law of ant foraging in nature. At the beginning of finding food, ants tend to blindly search. They secrete a kind of pheromone along the way, which can serve as a means of communication. Compared with ants choosing longer paths, ants choosing shorter paths can find food and return faster, leaving higher concentrations of pheromones on the path. Later ants tend to choose paths with higher pheromone concentrations to obtain higher efficiency. This is a positive feedback mechanism. The pheromone concentration on the optimal path will become higher and higher, while the pheromone concentration on other paths will dissipate over time. This is the basic idea of the ant colony algorithm. 3.1.1 State Transition Rule Ants depart from the starting point. The choice of the next path node is determined by the state transition function Pijk . It represents the probability of the k-th ant moving from point i to point j in each feasible direction. The roulette wheel method is used to determine the next traveling direction of the ant. The state transition function is defined as formula (2): ⎧ (τijk (t))α ·(ηijk (t))β ⎪ ⎨  k k α β , j ∈ Allowed (k) (2) Pijk = j∈Allowed (k) (τij (t)) ·(ηij (t)) ⎪ ⎩ 0, otherwise

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where Allowed(k) is the set of optional nodes available to the ant in the next step,τkij is the pheromone concentration on the path from i to j for ant k, ηij is the heuristic factor representing the distance from i to j for this ant, which can be expressed as formula (3): ηijk (t) =

1 dij

(3)

It represents the reciprocal of the distance from i to j. In the basic ant colony algorithm, the value of dij can only be 1 or 1.414. Alpha and beta are the pheromone concentration and heuristic factor weights respectively, controlling the relative importance of both in the state transition rule. 3.1.2 Pheromone Update Rule After one iteration of the population, the pheromone matrix needs to be updated. The pheromone accumulates as ants secrete it when passing by, and also evaporates over time. Therefore, the pheromone concentration update formula (4) can be expressed as: τij (t + 1) = (1 − ρ) · τij (t) + τij (t)

(4)

where ρ is the evaporation coefficient, representing the speed of pheromone evaporation, 1 − ρ is the amount of pheromone retained after one iteration of the population, τ ij is the change of pheromone in the current iteration, which can be expressed as formula (5): τij (t) =

M 

τijk (t)

(5)

k=1

where M is the total number of ants in one iteration. Therefore, the meaning of τij can be understood as the total amount of pheromone secreted by each ant in the current iteration. We take the total amount of pheromone q secreted by each ant every time as a constant, and the pheromone is secreted evenly along the path taken. Thus we can get the following formula (6): τijk (t) =

Q Lk

(6)

Therefore, formula (6) means that on the path taken by the k-th ant in this iteration, the amount of pheromone secreted on each path segment is q/Lk . 3.2 Basic Steps of Ant Colony Algorithm Step 1: Initialize the pheromone matrix, number of ants per generation, number of iterations, weight coefficients, etc. Step 2: Place M ants at the starting point. Step 3: In one iteration, for each ant in the current generation, determine the next node by roulette wheel selection based on pheromone concentration and heuristic factor. Add

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the previous node to the tabu list until the ant reaches the destination or the step limit. Store the feasible path and path length of this ant. Step 4: After one iteration of the population, update the pheromone matrix. Step 5: Repeat steps 2 to 4 until the iteration limit is reached to obtain the optimal path solution.

4 Improved Ant Colony Algorithm (IACO) The basic ant colony algorithm is prone to get stuck in local optima when solving robot path planning problems due to constant weight coefficients and distance heuristic factors that only consider local shortest paths. The lack of pheromone guidance in the initial stage leads to blind search, and the excessive randomness of the roulette wheel method makes it difficult for the algorithm to converge. To address these issues, this article adopts a series of strategies to improve the traditional ant colony algorithm. 4.1 Improvement Strategies for Ant Colony Algorithm This article introduces a direction selection rule based on distance factors to improve the defects of roulette wheel selection, introduces a new distance heuristic factor for easier convergence in later stages, adopts adaptive weight coefficient settings to avoid getting stuck in local optima, introduces stimulus probability and uneven initial pheromone distribution based on location information to avoid the blindness of early searches, introduces global scanning and directional angle constraint functions for good adaptability to complex maps, and finally adopts a fallback strategy to prevent deadlocks and improve the efficiency of each ant. 4.1.1 Direction Selection Rule Based on Distance Factors Compared with the previous state transition rule, the direction selection rule based on distance factors introduced in this paper effectively shortens the optimal path and time, and has certain advantages. Its principle is to use a random function to select between two rules alternately. It is also one of the key elements of typical ant colony system (ACS) theory. The related formulas are as follows:  arg max(((τ (r, μ))α · ((η(r, μ))β ), q < q0 (7) S= Pijk , otherwise where μ is the selectable direction of the current node, η(r, μ) is the distance between grid r and grid μ, τ(r, μ) is the pheromone between the two nodes, q is a constant between 0 and 1 determined by a random function. When q > q0, the state transition rule is used, otherwise the distance factor direction selection rule is used.

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4.1.2 Improvement of Distance Heuristic Factor The distance heuristic factor in traditional ant colony algorithm is only related to the distance between the selectable node and the destination. When the distance difference between selectable nodes and the destination is small, it cannot provide good guidance for ants’ next node selection. To address this issue, this article improves the distance heuristic factor to be related not only to the destination, but also to the starting point and current node. The improved formula is expressed as follows: ηijk (t) =

QL dsi + dij + dje

(8)

where dsi is the distance from the starting point to the current node, dij is the distance from the current node to the next node, dje is the distance from the next node to the destination, QL is a constant. The improved formula tends to make ants move along the straight line from starting point to destination. In this case, the distance heuristic factor is maximized, so in roulette wheel selection, ants are more likely to choose to move in this direction. 4.1.3 Global Scanning In the process of ants searching for paths, this article initially proposed a variable step size local visual scanning rule. This rule aims to allow ants to quickly find suitable step sizes in both narrow and open terrains, so as to converge faster, as shown in Fig. 3. The local ant visual scanning increases the adjacent eight grid locations that ants can scan to 16, and the distance that ants can travel changes to 1, 1.414, 2, 2.828. Ants will choose a suitable direction based on the pheromone concentration of the next node. Breaking the limitation of single step size gives ants a local vision towards the target point, which can effectively improve the searching ability of ants. However, there is also the problem of winding paths caused by uneven distribution of step size weights and node pheromones. To address this, this article further improves the global scanning proposed by Ma Feiyu, as shown in Fig. 4. The blue dot is the current node, and the green area is the globally reachable points in the visual field of the ant. The principle is to rotate a line passing through the geometric center of the current node for a whole circle. The area swept over by the line without hitting obstacles, i.e. the set of blank grids passed through by the line from the geometric center, are the next reachable points for the current node. This strategy can adaptively adjust the visual field of ants according to map changes, improving the map adaptability and convergence speed of the algorithm.

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Fig. 3. Local ant visual perspective

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Fig. 4. Ant global scanning

After improving the ant’s next node selection mechanism with the global vision, the number of optional nodes for ants has increased significantly, and the differences between adjacent optional nodes have diminished. This can easily lead to blind choices by the ants. To accelerate the convergence speed of the ant colony algorithm, this paper introduces a directional constraint function in the probability selection function. The directional constraint can improve the blindness of the ant colony’s search and speed up the convergence of the algorithm. As shown in Fig. 5, when an ant moves from the starting point to the target point, where A and B are the ant’s selectable next target nodes, the ant’s higher probability selection node can be determined based on the direction constraint angle formula.

Fig. 5. Direction constraint angle

The redesigned probability selection function is shown in Formula 9: ⎧ (τijk (t))α ·(ηijk (t))β φij (t) ⎪ ⎨  , j ∈ Allowed (k) k k α β k Pij = j∈Allowed (k) (τij (t)) ·(ηij (t)) φij (t) ⎪ ⎩ 0, otherwise

(9)

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The direction constraint function is shown in Formula 10: φij (t) =

Q → →

cos−1 →ij · ie→ ij · ie

(10) +1

where ij represents the direction vector from current node i to the next candidate node j, and ie represents the direction vector from current node i to the target point e. Q is a constant, α represents the pheromone concentration weight factor, and β represents the heuristic function weight factor. ϕij represents the direction constraint function. The direction constraint function increases the probability of exploring the region near the target point. In some cases, such as when the ant search path is deadlocked, the ant can explore other areas again without compromising the integrity of effective information. 4.1.4 Adaptive Weight Coefficients The larger the weight of pheromone concentration in the early stage of ant path searching, the faster ants can find the better paths from previous iterations. The larger the weight of distance heuristic factor in the later stage of path searching, the faster the convergence speed. Since fixed value weight coefficients cannot reflect the change in importance of pheromone concentration and distance heuristic factors at different stages when ants search paths, this article designs an adaptive weight coefficient function as follows: ⎧ k −k ⎨ Q1 − q1 · e k3 −e −k3 (11) α(k) = e 3 +e 3 , k ≤ k0 ⎩ C1 , otherwise ⎧ 1 ⎨ 1 +1 α(k) k , k ≤ k · e Q 2 0 (12) β(k) = ⎩ C , otherwise 2 where q1 , q2 , Q1 , C1 , C2 , k0 are constants, and k is the current number of iterations. 4.1.5 Initial Pheromone Distribution To address the blind search in the early stage of the algorithm, this article introduces the concept of stimulus probability and defines the initial pheromone matrix as follows: τij (0) = esp(i)−Die(i)−Dsi(i)

(13)

where sp is the stimulus probability, Die is the distance function from current point to the end point, Dsi is the distance function from the starting point to the current node.

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The stimulus probability sp is essentially a measure of how many obstacles are around a node. The more obstacles, the smaller the stimulus probability, and the less attractive the node is to ants.To give a formula for the stimulus probability, C(8, Nobs ) needs to be introduced first, which is calculated as: C(8, Nobs ) =

8! (8 − Nobs )! · Nobs !

(14)

where 8 refers to the 8 directions surrounding the current node. Nobs refers to the number of those 8 directions that are obstructed by obstacles around the current node. C(8 − Nobs − 1, 1) =

(8 − Nobs − 1)! (8 − Nobs − 1 − 1)!

(15)

where 8 − nobs − 1 represents the number of remaining selectable direction nodes for the ant after excluding the obstacle points and the previous node.sp can be represented by the following formula according to formula (14) and formula (15): sp =

C(8, Nobs ) C(8 − Nobs − 1, 1)

(16)

In addition to introducing the stimulus probability, this paper also gives some initial pheromone to the grid cells along the theoretical optimal path from the starting point to the target point, which ignores obstacles and helps accelerate the path optimization process of the algorithm. Since the principle of stimulus probability ignores the allocation of initial pheromones to the edge grid cells of the grid map, choosing edge grids as the endpoint can easily lead to the phenomenon of ants repeatedly circling around the endpoint vicinity when approaching the endpoint. To address this issue, this paper artificially strengthens the initial pheromone of the grids near the endpoint by adopting a radiating and decaying distribution of initial pheromones, which assigns initial pheromones to the endpoint and its surrounding grids in a radiating and decaying manner, effectively resolving this problem.

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4.1.6 Backtracking Strategy Ants often encounter situations where they walk into a U-shaped trap or are surrounded by obstacles, leaving them unable to choose the next node during their forward movement. To address this issue, this paper introduces a backtracking strategy to allow ants to retreat to the previous node when trapped in a U-shaped trap. As shown in Fig. 6, when the ant reaches grid 1 and finds no selectable next node, it needs to retreat to the previous node, delete grid 1 from the tabu table, and reselect an appropriate next node. If still no reachable node is available, it continues retreating. The idea of backtracking greatly enhances the search capability of each ant and improves the capability of the ant colony to handle complex environments to a large extent.

Fig. 6. Backtracking strategy

4.2 Pseudocode and Flowchart of the IACO Start, initialize the algorithm parameters, conduct preprocessing, mainly including the initial pheromone distribution and the distance heuristic factor. Then, for each ant, select the appropriate node with the method of global scanning, and finally reach the end point. If the ant is stuck in a deadlock during the process, go back to the previous node. An ant updates the pheromone distribution on the map after the search. This step is repeated thereafter until the iteration ends. The best path is finally obtained (Fig. 7).

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Fig. 7. Flowchart of IACO

5 Simulation Experiments and Result Analysis This paper simulates and tests the improved ant colony optimization algorithm (IACO) on Matlab R2021b running on Windows 10 64-bit operating system, AMD Ryzen 7 4800H with Radeon Graphics 2.90 GHz processor, 16.0 GB RAM. The main comparison metrics are average path length, path length stability, runtime, and number of iterations. Where IACO is the ant colony improvement algorithm in this paper, ACS [16] is ant colony system, MMAS [17] is max-min ant colony algorithm, LVACO is the algorithm

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with local vision instead of global vision under the same other strategies, and OACO is the algorithm in reference [14]. The main parameter settings of the algorithm are shown in Table 1: Table 1. Parameter setting parameter

Set value

parameter

Set value

K

100

Q1

2.5

M

50

C1

1.5

Q

1

Q2

1.5

k0

10

C2

6

q0

0.6

QL

10

ρ

0.3

To test the optimization effect of the algorithm, this paper mainly adopts eight complex grid maps according to reference [18] for different test indicators, as shown in Fig. 8(a) to 8(h). The grid maps selected in this paper are all 30 × 30 in size, with black representing obstacles. The red polyline planned in the figure is the optimal path planned by the improved ant colony algorithm in this paper. It can be seen from the path planning in the figures that the improved ant colony algorithm in this paper works well for complex corridor-type maps. This paper performs 20 comparative experiments on four 30 × 30 maps and takes the average. The results are as follows (Table 2): It can be seen from Table 1 that the algorithm in this paper has superior average path length and runtime performance on most maps. However, on some simpler maps, it may occasionally be inferior to classic improved algorithms, mainly because the feasible paths on the map are single, and the strategies of the improved algorithm in this paper require some time to run. The comparison between the algorithm in this paper and the local ant colony algorithm LVACO with control strategy variables shows the superiority of global vision over local vision. In Map 8, the average path length of LVACO far exceeds that of the improved algorithm in this paper, but the runtime is only about half. The reason is that LVACO cannot fully explore the map area to converge globally. Compared with OACO which also has global vision, the algorithm in this paper optimizes its strategic ideas and improves candidate nodes and path movements. The results show that the average path length and runtime of the algorithm are superior to those of OACO.

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Figure 9 shows the convergence curve trends, where the yellow convergence curve is the improved ant colony algorithm in this paper, the blue curve is the algorithm in reference [14], and the red curve is the local vision algorithm. It can be seen from the figure that the algorithm in this paper can converge faster and has the optimal path length. Due to the complexity of the map, the classical and maximum minimum colony algorithms do not reach convergence, and therefore they are only compared among the three algorithms.

Fig. 8. Path planning of the improved ant colony algorithm

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Fig. 8. (continued)

Table 2. Algorithms comparison Algorithm

Maps

Average length

IACO

Map 1

53.468

ACS

Map 1

MMAS LVACO

Time

Algorithm

Maps

Average length

Time

8.383

IACO

Map 5

46.829

8.143

65.5796

10.285

ACS

Map 5

48.2441

10.794

Map 1

108.3083

10.039

MMAS

Map 5

67.7737

9.846

Map 1

92.3779

13.391

LVACO

Map 5

72.6021

9.604

OACO

Map 1

57.1565

9.123

OACO

Map 5

53.9754

10.890

IACO

Map 2

54.6601

9.571

IACO

Map 6

45.9180

7.913 (continued)

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C. Can et al. Table 2. (continued)

Algorithm

Maps

Average length

Time

Algorithm

Maps

Average length

Time

ACS

Map 2

76.5534

11.272

ACS

Map 6

57.9754

10.890

MMAS

Map 2

109.4254

11.055

MMAS

Map 6

74.4794

9.744

LVACO

Map 2

103.8188

12.945

LVACO

Map 6

75.9151

10.143

OACO

Map 2

65.5796

10.28

OACO

Map 6

48.2316

10.439

IACO

Map 3

55.2463

IACO

Map 7

102.9939

14.943

244.4716

18.286

7.778

ACS

Map 3

66.6885

10.454

ACS

Map 7

MMAS

Map 3

83.1235

10.005

MMAS

Map 7

LVACO

Map 3

76.1144

10.730

LVACO

Map 7

201.5246

15.678

OACO

Map 3

61.5732

7.74

OACO

Map 7

103.2568

15.872

IACO

Map 4

43.9915

7.531

IACO

Map 8

131.1017

27.082

ACS

Map 4

43.2967

9.541

ACS

Map 8

MMAS

Map 4

64.1495

10.295

MMAS

Map 8

LVACO

Map 4

55.7997

9.024

LVACO

Map 8

229.118

15.82

OACO

Map 4

48.2441

10.794

OACO

Map 8

137.2765

30.293

Fig. 9. Convergence curve

Figure 10 compares the stability of five algorithms. The higher the coincidence between the optimal and worst paths, the better the stability of the algorithm. The algorithms with zero average path length failed to converge on those maps and did not obtain

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map(a)

map(b)

map(c)

map(d)

map(e)

map(g)

123

map(f)

map(h)

Fig. 10. Stability comparison of 5 algorithms

a relatively ideal average path length. It can be seen from the figure that the IACO algorithm in this paper has better stability and outperforms the compared algorithms.

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6 Conclusions and Future Work This paper aims to address the issues of ant colony optimization such as slow initial convergence speed, easy entrapment in local optima, long runtime, and poor adaptability to complex maps. Strategies including adaptive, improved global scanning, stimulus probability, and path backtracking are introduced to improve the ant colony algorithm, optimizing the blindness of early search and accelerating the convergence speed. Simulation results show that the improved ant colony algorithm in this paper has general superiority over traditional algorithms and compared algorithms, and is suitable for complex corridor-type maps. This paper optimizes the ideas in reference and ultimately achieves better simulation results than the original global vision algorithm. In the future, machine learning, game theory and integration with other intelligent optimization algorithms can be combined to further improve the algorithm in this paper. Financial Support and Sponsorship. The authors appreciate the support of National key research and development program (2022YFB4602104), National Natural Science Foundation of China (62076095, 61973120).

References 1. Yu, Z., Yan, J., Zhao, J., et al.: Path planning of mobile robots based on improved artificial potential field method. J. Harbin Inst. Technol. 43(01), 50–55 (2011) 2. Li, M.: Research on UAV mission planning methods based on intelligent optimization and RRT algorithms. Nanjing University of Aeronautics and Astronautics (2012) 3. He, Q., Li, N., Luo, W., et al.: A survey of machine learning algorithms in the era of big data. Pattern Recogn. Artif. Intell. 27(04), 327–336 (2014) 4. Gao, H., Feng, B., Zhu, L.: Solving TSP problem with intelligent optimization algorithms. Control Decis. 2006(03), 241–247+252 (2006) 5. Ge, J., Qiu, Y., Wu, C., et al.: A review of genetic algorithm research. Comput. Appl. Res. 2008(10), 2911–2916 (2008) 6. Sun, Z., Xue, L., Xu, Y., et al.: A review of deep learning research. Comput. Appl. Res. 29(08), 2806–2810 (2012) 7. Duan, H., Wang, D., Zhu, J., et al.: Progress in ant colony algorithm theory and application research. Control Decis. 2004(12), 1321–1326+1340 (2004) 8. Yang, W., Li, Q.: A review of particle swarm optimization algorithms. Chin. J. Eng. Sci. 05, 87–94 (2004) 9. Zhou, Z., Lu, H.: Improved ant colony algorithm for path planning in complex environments. Comput. Eng. Des. 32(05), 1773–1776 (2011) 10. Sun, C., Lin, J., Lou, G., et al.: Dual ant colony algorithm with complete crossover for global path planning of concave obstacles. Trans. Chin. Soc. Agric. Mach. 07, 149–153 (2008) 11. Liu, T., Cheng, R.: Path planning of robots with elitist strategy and visual detection ant colony algorithm. Comput. Appl. 209(01), 92–93+96 (2008) 12. Akka, K., Khaber, F.: Mobile robot path planning using an improved ant colony optimization. Int. J. Adv. Robot. Syst. 15(3), 172988141877467 (2018) 13. Ma, S., You, X., Liu, S.: Ant colony algorithm with dynamic pheromone updating and path reward-penalty. Comput. Eng. Appl. 59(04), 64–76 (2023) 14. Ma, F., Qu, Z.: Mobile robot path planning research based on heterogeneous double population global vision ant colony algorithm. Comput. Appl. Res. 39(06), 1705–1709 (2022)

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15. Wang, Z., Xia, X.: Application of adaptive ant colony algorithm in robot path planning. J. Minnan Normal Univ. (Nat. Sci. Edn.) 35(03), 38–45 (2022) 16. Ma, X., Mei, H., Gong, R., et al.: Research on the path planning of mobile robots based on an improved ACS algorithm. J. Hunan Univ. (Nat. Sci. Edn.) 48(12), 79–88 (2021). https:// doi.org/10.16339/j.cnki.hdxbzkb.2021.12.010 17. Fang, J.: MMAS improved algorithm based on greedy edge and its application in TSP. Softw. Guide 17(08), 97–101 (2018) 18. Wang, X.W., Wei, J.B., Zhou, X., Xia, Z.L., Gu, X.S.: AEB-RRT*: an adaptive extension bidirectional RRT* algorithm. Auton. Robot. 6, 685–704 (2022). https://doi.org/10.1007/S10 514-022-10044-X

ACB-RRT*: Adaptive Companion Points Bidirectional RRT* Algorithm Junnan Jiang, Heng Zhi, Xiaoxiang Tang, Can Cui, and Xuewu Wang(B) Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China [email protected]

Abstract. To solve the problems of long search time, poor convergence and many redundant points in the RRT* algorithm, an adaptive companion point bidirectional RRT* algorithm (ACB-RRT*) is proposed. This algorithm adopts a hybrid strategy which including target-biased, dynamic step, and companion point generation. The target-biased strategy takes the target node as a sampling point according to the size of the random sampling probability to enhance the guidance. The dynamic step adopts different step sizes for expansion according to the random sampling probability to accelerate the convergence of the algorithm. The companion point generation determines multiple corresponding companion points based on the obtained expanded point to reduce the number of iterations. It also dynamically adjusts the angle of generating companion points based on the number of failed expansions. After obtaining a feasible path, trajectory optimization is performed on it. Greedy algorithm and cubic B-spline curve fitting are used to optimize nodes and smooth the trajectory, and finally an optimal collision-free path is obtained. Compared with RRT, RRT*, RRT-GoalBias, and B-RRT* algorithms, the results show that ACB-RRT* algorithm outperforms them in search time, path length, and number of iterations, indicating the superiority of this algorithm. Additionally, the algorithm has been successfully applied to welding scenarios. Keywords: Path planning · RRT* · Hybrid strategy · Trajectory optimization

1 Introduction With the continuous innovation of science and technology and the advent of the fourth industrial revolution, intelligent robotics has been developed rapidly and is being used in a large number of industries such as industry, manufacturing, agriculture and service industry. As one of the key technologies in artificial intelligence, path planning has received a lot of attention in the industry. Path planning refers to finding a collisionfree and relatively optimal path between the starting and ending points in the robot’s workspace. Since the path planning algorithm was proposed, many mature algorithms

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. Chen et al. (Eds.): RWIA 2022, TIWM, pp. 126–145, 2024. https://doi.org/10.1007/978-981-99-9629-2_8

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have emerged. For example, RRT algorithm [1], A* algorithm [2, 3], PRM algorithm [4], Dijkstra algorithm [5], Artificial potential field algorithm [6], Ant colony algorithm [7], Particle Swarm Optimization [8] and genetic algorithm [9] etc. Among them, the Rapidly-Expanding Random Tree algorithm (RRT) can find a path relatively quickly in a high-dimensional environment with high flexibility and requires fewer parameters, which is very suitable for completing path planning in a high-dimensional space. In recent years, for the shortcomings of the RRT algorithm, experts around the world have proposed improvement strategies from different aspects. From the aspect of path quality, the asymptotically optimal RRT* algorithm was proposed by Karaman et al. [10, 11]. From the aspect of node selection, the RRT-GoalBias algorithm is proposed [12, 13], which can control the probability of occurrence of the goal points and reduce the convergence time of the algorithm greatly. Some scholars have proposed a bidirectional RRT algorithm based on the bidirectional idea [14–16], and Kuffner et al. proposed the RRT-connect algorithm [17], which expands two growing trees simultaneously from the starting point and the goal point by applying a greedy strategy. Similarly, Jordan et al. improved the RRT* algorithm by proposing the B-RRT* [18], and later the RRT* algorithm with bidirectional bias was proposed by [19]. Xuewu Wang et al. proposed an adaptive extended bidirectional RRT* algorithm (AER-RRT*) [20] based on the previous work. Li et al. [21]proposed a bidirectional RRT algorithm based on target search, allowing the algorithm to converge more quickly, and the idea proposed in this article will also consider it as one of the strategies. Liu, X. D. et al. used a directionbased strategy [22], which reduces the number of iterations to a larger extent. Zhang [23] proposes the Dynamic RRT algorithm, which aims to plan a feasible path while balancing the convergence time and path length in an environment with randomly distributed obstacles. In addition some scholars combined the RRT algorithm with some intelligent strategies, and some scholars combined the artificial potential field algorithm with the RRT algorithm [24]. In addition, some scholars combined the RRT algorithm with some intelligent strategies. Based on the above, this paper optimizes the existing algorithm strategy and proposes an improved algorithm based on RRT* algorithm by combining the comprehensive optimization strategy of target-biased, dynamic step and companion point generation, so as to achieve the improvement purposes of reducing the randomness of sampling point selection, increasing the rate of stepping and reducing the number of iterations. And combining the greedy idea and cubic B-spline curve fitting to the path optimization process.

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2 Problem Definition Given a space Q ⊂ Rd where d represents the dimension of the space. The space Q can be further divided into free space Qfree and obstacle space Qobs . Euclidean distance is used to calculate the distance between two nodes, expressed as EucDis(q1 , q2 ), where q1 and q2 represent the two nodes in the free space (q1 ∈ Qfree , q2 ∈ Qfree ). qinit and qgoal are the starting and ending points of the path planning. Since the path planning process uses sampling points for point selection, the ending point is not selected accurately to be qgoal , leading to the algorithm not converging and path search failure. Therefore, a threshold distance needs to be set, expressed as disth, to ensure that the path can be searched successfully, i.e., when the Euclidean distance between the sampled point and the ending point is less than the threshold distance, the path search is successful. Moreover, the two points connected in the path cannot have collision. Define any two points q1 and q2 , the collision-free path between any two points is path : [0, 1], where path(0) = q1 , path(1) = q2 . The collision-free path from starting point qinit to the ending point qgoal is defined as: path(0) = qinit , path(1) = qgoal , ∀t ∈ [0, 1], path(t) ∈ Qfree . Also two growing trees are defined as T1 = (V1 , E1 ) ⊂ Q, T2 = (V2 , E2 ) ⊂ Q, where V1 , V2 , E1 , E2 both belong to Qfree , and V1 , V2 represent the set of all nodes in the tree, the E1 , E2 represent the set of edges connecting these nodes. The purpose of the algorithm is to find a feasible path path : [0, 1] (path(0) = qinit , path(1) = qgoal ), ∀t ∈ [0, 1], path(t) ∈ Qfree , and make the path length, path search time, and the number of sample point iterations as small as possible.

3 ACB-RRT* Algorithm For the RRT algorithm, the randomness of the path search process leads to low search efficiency. While the improved search ability of the RRT* algorithm comes at the significant cost of increased search time. Therefore, considering the insufficient path search efficiency of the RRT and RRT* algorithms, the bidirectional RRT algorithm improves the efficiency of the algorithm significantly compared to the RRT. This paper combines the target-biased and companion point. Through adaptive bidirectional expansion search, a collision-free path from the starting point to the ending point is obtained. Finally, node optimization is performed using greedy thinking, and the path is smoothed using cubic B-spline curves to obtain an optimal collision-free path. The pseudocode of the algorithm is as follows. The flowchart is shown in Fig. 1.

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3.1 Target-Biased The ACB-RRT* algorithm uses a probabilistic target-biased in expanding nodes, as shown in Eq. (1). In the process of expanding the nodes, different node generation schemes are selected according to the magnitude of the random sampling probability.  qpoint if p > P0 (1) qrand otherwise where P0 is the preset probability threshold and p is the random sampling probability. qpoint is the latest node of another growing tree and is updated with the update of the nodes in the growing tree. Its definition is as follows.

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Fig. 1. Algorithm flow chart

Expand the nodes of the growing tree T1 by the target-biased sampling method to obtain a round of expanded points q1 (also called the last round of search). Based on the expanded point q1 obtained in the last round, determine q1 as the target point qpoint for expansion of T2 , that is, make qpoint = q1 , and use it as the expansion of this round of T2 to obtain the expanded point q2 . Update qpoint and determine q2 as the target point qpoint for expansion, that is, make qpoint = q2 , use it as the expansion of T1 . As shown in Fig. 2, taking the expansion of the growing tree T2 from the ending point qgoal as an example, qpoint is the node expanded in the last round of the random growing tree T1 . This strategy can enable the two growing trees T1 and T2 to expand toward each other so that the algorithm can converge faster.

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q point

q new q near q goal

Fig. 2. Target-biased

3.2 Dynamic Step When there are fewer obstacles or no obstacles within a certain area around the expanded point, using a fixed step size strategy to expand node will inevitably increase the path planning time and reduce the efficiency of path search. Therefore, this paper adopts a dynamic step size strategy. The strategy description is as follows. If the random sampling probability p is greater than the preset probability threshold P0 , start from the nearest point, i.e., the nearest node qnear between the current expanded growing tree and the target point. Expand towards the latest node qpoint of the other growing tree and 2 times the preset step size as the step size. If the step size is too long, it is easy to hit an obstacle and fail to obtain a new expanded point. If the step size is too short, the improved search effect is insufficient. The generation of expanded points is shown in Fig. 3. If the random sampling probability p is not greater than the preset probability threshold P0 , generate a random point qrand in the free space Qfree of the workspace. Start from the nearest point, take the generated random point qrand as the target point. Offset toward the target point with the preset step size to generate the expanded points. That is, when the random sampling probability p > P0 , take qpoint as the target point while making the step size step = 2 ∗ step. Increase the search speed of the two growing trees and further accelerate the convergence of the algorithm.

2*step q near q new

Fig. 3. Dynamic step size

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3.3 Companion Point Generation To reduce the time consumption caused by point selection and the generation of redundant point during the path search process, this paper improves the idea based on directionbased. Based on the expanded point obtained in this round, the corresponding multiple companion points are determined. The expansion process is as follows. Where qinit is the starting point, qgoal is the ending point, qrand is a randomly generated point in the space, qnear is the nearest point, qpoint is the target point, the random sampling probability p, and the probability threshold P0 . When expanding nodes, if p > P0 , determine three companion points. The distances between the three companion points and the nearest point are 1 time the preset step size. One companion point is located at the midpoint between the expanded point and the nearest point. The angles between the lines connecting the remaining companion point and the nearest point and the line connecting the target point and the nearest point are equal to the initial angle θ1 . As shown in Fig. 4(a), The expanded point q1 is obtained by offsetting the nearest point qnear toward the latest node qpoint of the other growing tree by 2 times the preset step size. There is 1 companion point q2 at the midpoint between the expanded point q1 and the nearest point qnear . The other 2 companion points q3 and q4 and the lines connecting q3 − qnear and q4 − qnear to the nearest point qnear have a length of 1 time the preset step size. The angles  q3 qnear q1 and  q4 qnear q1 of the lines are equal to the initial angle θ1 . When expanding nodes, if p ≤ P0 , determine two companion points. The distances between the two companion points and the nearest point are 1 time the preset step size. The angles between the lines connecting the two companion points and the nearest point and the line connecting the target point and the nearest point are equal to the initial angle θ1 . As shown in Fig. 4(b), the expanded point q1 is obtained by offsetting the nearest point qnear toward the generated random point qrand by 1 time the preset step size. The lines connecting the two companion points q2 and q3 to the nearest point qnear , q2 − qnear and q3 − qnear , have a length of 1 time the preset step size. The angles of the lines  q2 qnear q1 ,  q3 qnear q1 are equal to the initial angle θ1 . Furthermore, the above initial angle θ1 is not the final expansion angle θ . The final expansion angle θ is a dynamic. It changes dynamically based on the number of collisions of the sampling points and based on the initial angle θ1 . When the number of collisions increases, the expansion angle θ also increases, so that the generated companion points can stay away from obstacles, more easily obtain non-colliding sampling points, improve the success rate of this sampling, and reduce the number of iterations. When the number of collisions reaches the set s, it means that the attraction of the target point hinders the growth of the tree, so the point is discarded. The expression of θ is as follows: θ = θ1 + (θ2 − θ1 ) ∗ e

−

3 (n+1)2

(2)

where θ1 and θ2 represent the maximum and minimum values of θ , respectively, and n is the number of collisions (0 < n < s). According to Eq. (2), it can be found that as n increases, θ increases continuously. When the number of collisions n is small (0 < n < s), the amplitude of change of θ is large. If n > s, the amplitude of change of θ is small, and it indicates that the attraction of the target point causes the algorithm

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to deadlock, so the point is discarded. Considering the optimal initial angle size, if the initial angle value is too small, the companion point is too close to the expanded point. When the expanded point fails to pass collision detection, the companion point also has a high probability of failing to pass collision detection, which cannot effectively improve the probability of obtaining new nodes that can pass collision detection in each search round. If the initial angle value is too large, the companion point may lose the tendency to approach the target point orientation, which may cause path redundancy, i.e. taking unnecessary detours, increasing processing time. Therefore, the values of θ1 , θ2 are taken as 60 and 90, respectively. And θ varies with n, so n is taken as 5. Perform collision detection one by one for the expanded points and corresponding companion points obtained in this round.

q near

q2

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(a) case 1

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Fig. 4. Schematic diagram of algorithm expansion

3.4 Node Optimization The RRT* path search algorithm can find a suitable path. However, due to its step size expansion search characteristic, its path is relatively tortuous. To improve the usability of the algorithm path, the obtained feasible path needs to be optimized. By greedy thinking, redundant points of the obtained feasible path are removed to find the key nodes of the path, as shown in Fig. 5. (1) Use the ACB-RRT* algorithm to find a feasible path. the set of nodes is Q : [q1 , q2 , q3 . . . qn ]. (2) Detect and traverse the subsequent nodes in turn from the starting point q1 . If q1 and qj have no collision, continue to traverse to the next node until the node qi collides with obstacles, where i, j ∈ [1, 2, 3, . . . , n], add node qi−1 to the new path node set NewQ. Start from node qi−1 and continue traversing backward until connecting to the end point. (3) Obtain a new path node set NewQ.

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q2

q1

Fig. 5. Node optimization strategy

3.5 Trajectory Smoothing The new set of path nodes NewQ obtained by the above node optimization strategy. The path formed by this paper is optimized in terms of path length, but there are still turning points and the path is not smooth enough, so this paper uses the method of cubic B-spline curves to smooth the turning points and improve the continuity and smoothness of the path. The general formulation of the B-spline curve is that there are n + 1 control points Pi where i ∈ [0, 1, 2, 3, . . . , n] and a node vector t = {t0 , t1 , t2 , . . . , tm }, in order to connect n+1. The control points can form a characteristic polygon, and the expression of the k-th B-spline curve is shown in Eq. (3), satisfying 2 ≤ k ≤ n + 1, and m = n + k + 1. P(t) =

k i=0

Pi · Fi,k (t), t ∈ [0, 1]

(3)

In Eq. (3), the Fi,k (t) is the k-th B-spline basis function, defined as Fi,k (t) =

1 k−i j (−1)j · Ck+1 · (t + k − i − j)k , t ∈ [0, 1] j=0 k!

(4)

Using a cubic B-sample curve, i.e., making k = 3 in Eq. (4), we obtain the cubic B-spline curve equation: P(t) = P0 · F0,3 (t) + P1 · F1,3 (t) + P2 · F2,3 (t) + P3 · F3,3 (t) ⎧ F0,3 (t) = ⎪ ⎪ ⎨ F1,3 (t) = ⎪ F (t) = ⎪ ⎩ 2,3 F3,3 (t) =

1 (1 − t)3 6 1 3 2 6 3t − 6t + 4 1 3 2 6 −3t + 3t + 3t 1 3 6t

+1

A comparison of the optimized paths is shown in Fig. 6.



(5)

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(a) Before smoothing

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(b) After smoothing

Fig. 6. Diagram of path smoothing comparison

4 Algorithm Test and Analysis In order to verify the superiority of the ACB-RRT* algorithm, simulation experiments are performed using Matlab R2022a and tested in two-dimensional space and threedimensional space, respectively. Two scenarios are used to validate this improved algorithm in 2D and 3D space, the environment of scenario 1 is equipped with fewer obstacles and the environment of scenario 2 is equipped with more obstacles, and the simulation experiments of path planning are performed for RRT, RRT*, RRT-GoalBias, B-RRT*, and ACB-RRT*, respectively. The blue lines are all the paths included in the growing tree, and the red lines are the planned paths solved by the algorithm. 4.1 Two-dimensional Space Simulation Experiments The size of the simulation map is 500 × 500, the starting point is (10, 10), the ending point is (490, 490), the probability threshold P0 = 0.8, expansion step step = 20, the black area indicates the obstacle area, and the white area indicates the obstacle-free area. To ensure the accuracy of the simulation experiments, the number of experiments for each path planning is 50, and the average value is taken and the data is recorded in Table 1 the experimental results are shown in Figs. 7 and 8: RRT, RRT*, RRT-GoalBias, B-RRT*, and ACB-RRT*, respectively. In Scenario 1, compared to the RRT algorithm, the average planning time of the path is reduced by 82.07%, the path length is reduced by 22.22%, and the number of iterations is reduced by 92.54%; compared to the RRT* algorithm, the average planning time of the path is reduced by 96.49%, the path length is reduced by 0.72%, and the number of iterations is reduced by 92.77%; compared to the RRT- GoalBias algorithm, the average planning time of the path is reduced by 13.74%, the path length is reduced by 15.18%, and the number of iterations is reduced by 51.43%,; compared to the B-RRT* algorithm, the average planning time of the path is reduced by 18.55%, the path length is reduced by 7.10%, and the number of iterations is reduced by 53.42%.

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(a) RRT

(b) RRT*

(d) B-RRT*

(c) RRT-GoalBias

(e) ACB- RRT*

Fig. 7. Diagram of path planning in two-dimensional simple space

(a) RRT

(b) RRT*

(d) B-RRT*

(c) RRT-GoalBias

(e) ACB- RRT*

Fig. 8. Diagram of path planning in two-dimensional complex space

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Table 1. Experimental data table for two-dimensional scenes Algorithm

Environment

Average planning time/s

Path length/unit length

Number of iterations

RRT

Scenario 1

0.8226

891

1367

Scenario 2

0.8137

947

1391

Scenario 1

4.2020

698

1411

Scenario 2

4.0312

736

1388

RRT-GoalBias

Scenario 1

0.1710

817

210

Scenario 2

0.2074

892

282

B-RRT*

Scenario 1

0.1811

746

219

Scenario 2

0.1821

781

286

Scenario 1 Scenario 2

0.1475 0.1638

693 734

102 141

RRT*

ACB-RRT*

In Scenario 2, compared to RRT, RRT*, RRT-GoalBias, B-RRT*, the average planning time in paths is reduced by 79.87%, 95.94%, 21.02%, 10.05%; the path length is reduced by 22.49%, 0.27%, 17.71%, 6.02%; the number of iterations is reduced by 89.86%, 89.84%, 50.00%, 50.70%. The ACB-RRT* algorithm has advantages in path length, path planning time and number of iterations, and its generated companion points greatly improve the success rate of expansion, thus reducing the number of iterations; moreover, the target point and dynamic step length strategy accelerate the convergence of the algorithm and shorten the time, combining with the experimental data and operation diagrams, the ACB-RRT* algorithm has a greater comprehensive performance in terms of advantage. 4.2 3D Space Simulation Experiment The size of the simulation map is 600 × 600 × 600, the starting point is (10, 10, 10), the ending point is (550, 550, 550), and the expansion step size step = 20, and other parameters are consistent with two dimensions. The experimental results are shown in Figs. 9 and 10, and the experimental data are recorded in Table 2. In Scenario 1, compared to RRT, RRT*, RRT-GoalBias, and B-RRT*, the average planning time in paths is reduced by 95.38%, 98.32%, 8.36%, and 36.83%, respectively; the path lengths are reduced by 27.02%, 7.79%, 15.19%, and 8.47%, respectively; and the number of iterations is reduced by 97.38%, 97.57%, 69.31%, and 67.23%, respectively. In Scenario 2, compared to RRT, RRT*, RRT-GoalBias, and B-RRT*, the average planning time in paths is reduced by 90.86%, 97.28%, 15.16%, and 28.44%, respectively; the path length is reduced by 25.33%, 4.60%, 12.63%, and 6.35%, respectively; and the number of iterations is reduced by 97.09%, 97.07%, 71.02%, and 65.02%, respectively.

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(a) RRT

(b) RRT*

(d) B-RRT*

(c) RRT-GoalBias

(e) ACB-RRT

Fig. 9. Diagram of path planning in three-dimensional simple space

(a) RRT

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(c) RRT-GoalBias

(e) ACB-RRT*

Fig. 10. Diagram of path planning in three-dimensional complex space

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Table 2. Experimental data table for three-dimensional scenes Algorithm

Environment

RRT

Scenario 1 Scenario 2

Path length/unit length

Number of iterations

2.8699

1347

2210

3.3679

1362

2439

Scenario 1

7.8759

1066

2385

Scenario 2

11.3335

1066

2424

RRT-GoalBias

Scenario 1

0.1447

1159

189

Scenario 2

0.3628

1164

245

B-RRT*

Scenario 1

0.2099

1074

177

Scenario 2

0.4301

1086

203

Scenario 1 Scenario 2

0.1326 0.3078

983 1017

58 71

RRT*

ACB-RRT*

Average planning time/s

By comparing the simulation experiments conducted by the algorithm in environments with different levels of complexity, it can be concluded that the ACB-RRT* algorithm has more excellent results in path planning, with great superiority in terms of path search time, path length and its quality, and the number of iterations, and the comparative graph of algorithm operation data is shown in Fig. 11. 1400 2D Scenario 1 2D Scenario 2 3D Scenario 1 3D Scenario 2

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RT B-R

Fig. 11. Diagram of algorithm running data comparison

4.3 Complexity Analytics of ACB-RRT* Complexity analysis primarily involves analyzing the time and space complexities of algorithms to assess their resource consumption. Time complexity reflects the time required for an algorithm to execute. It is commonly expressed asymptotically using big O notation as the problem size grows. For example, O (n) denotes linear growth with the problem size n, while O (n2 ) denotes quadratic growth with n. Time complexity is concerned with the order of magnitude of the dominant operations. Space complexity reflects the memory required for an algorithm to execute. It is also expressed using big O notation. For instance, O (n) denotes linear space complexity. Complexity analysis generally examines the worst-case, average-case, and best-case scenarios. The worst-case

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reflects an algorithm’s performance on the most difficult inputs, average-case considers general inputs, and best-case shows performance on ideal inputs. Analyzing algorithm complexity facilitates comparison of time and space costs across different algorithms, providing a basis for design and optimization. For a given problem, algorithms with lower complexity are generally superior within their class. 4.3.1 Time Complexity The primary steps in ACB-RRT* consist of: Sample, NearVertex, Extend , Collision, NearVertices, GetSortedList, ChooseBestParent, InvertVertex and RewireVertices. A detailed analysis of the time complexities of the main steps is as follows. Given N sampled points, the simple operations of Sample, and Extend enable linear-time performance. Hence, their time complexities are O (N ). It has been proven that NearestVertex and NearestVertices have time complexities of O (N ∗logN ). CollisionFree involves testing whether the new extension point lies in the free space Qfree and if the path between two points is collision-free with obstacles, i.e. path : [0, 1], path(0) = qinit , path(1) = qgoal , ∀t ∈ [0, 1], path(t) ∈ Qfree . Its time complexity is O (N ∗ logN ). GetSortedList sorts path lengths to other nodes in the vicinity, while ChooseBestParent selects the parent node qmin of qnew . Relative to the total samples, the neighborhood size is small. Their time complexities can thus be assumed as O (N ). InvertVertex and RewireVertices also exhibit O (N ) time complexity. Therefore, by the rules of big O notation, the asymptotic time complexity of ACB-RRT* is O (N ∗ logN ). 4.3.2 Space Complexity Given a total of N sample points, the space complexity of ACB-RRT* is the storage needed for all nodes and edges, i.e., O (|V | + |E|) where |V | and |E| are the number of vertices and edges. Since |V | and |E| are bounded by O (N ), the space complexity of ACB-RRT* simplifies to O (N ).

5 Scenario Test Based on the understanding and analysis of various systems and algorithms [25, 26], an offline simulation platform for industrial robots was developed to simulate the practical effects of applying the algorithm to welding scenarios, on the basis of existing achievements. This platform belongs to the field of offline programming for industrial robots, and can be used for simulation teaching of industrial robot arms and simulating path planning effects of intelligent planning algorithms. The modular architecture of the platform functions is shown in Fig. 12. The main interface of the platform is shown in Fig. 13. Based on Matlab’s APP designer component, this platform was developed as a standalone desktop application called Autonomous Industrial Robot Optimization Platform, to improve the efficiency and practicality of offline programming teaching. The platform incorporates modules for model loading, robot teaching, point information, intelligent path planning, system simulation, and collaborative simulation. Through coordination between these modules, teaching tasks can be completed. Through the user interface, the operator can construct and simulate the project scene by controlling the

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model visualization module, use the simulation teaching module to finely adjust the robot poses and save the point information where the robot is located, the point information module can import and export the taught point information and perform operations like add, delete and modify on the recorded point data. The intelligent path planning module uses intelligent planning algorithms to plan a collision-free path to achieve the desired robot operation. The system simulation module simulates the planned workflow. The collaborative simulation module transfers the taught point data from the platform to RobotStudio for joint simulation, and can directly convert the teaching program into formats that can be used in RobotStudio, which facilitates dynamic adjustment of the teaching program based on results from the joint simulation. Experiments show that through coordination between modules, especially integration of intelligent path planning algorithms, the platform simplifies the offline programming teaching process and improves efficiency. The simulation scenario uses two gusset plates as the target workpieces. Workpiece 1 and Workpiece 2 each have two weld seams, and the positions of the workpieces and weld seams are known. To verify the effectiveness of the proposed method in actual welding tasks, simulation is carried out using the platform. The simulation requires the algorithm to search for collision-free transition paths to complete the predetermined welding of the workpieces by the ABB IRB 120 robot within a limited time. To improve the efficiency of searching for feasible paths, the gun-raising motion is applied in the welding simulation scenario to reduce the probability of collision during the motion of the arc welding robot. The gun-raising height is set to 60mm for Workpiece 1 and Workpiece 2. Within the premise of collision avoidance, the path searched by the ACBRRT* algorithm is almost a straight line to reach the target point, making the path as short as possible, as shown in Fig. 14. The blue coordinate points RT_tp1 to RT_tp10 are key points after path optimization, and the red line is the simulated path. After multiple simulations, the ACB-RRT* algorithm has strong search capability and adaptability to complex scenarios, and can effectively and efficiently solve various collision-free path planning problems.

Fig. 12. Platform Functional Module Architecture

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Fig. 13. Platform main interface and function introduction

Fig. 14. Path simulation result

6 Conclusion For the path planning based on RRT* algorithm, the following problems are prominent: longer path search time, lower efficiency of the algorithm, more redundant points generated, and poorer generated paths. To address these problems, this paper combines

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the target-biased, companion point and dynamic step to reduce the number of iterations of the algorithm so that the path planning time, path quality and number of iterations are largely better than those of RRT, RRT*, RRT-GoalBias and B-RRT*. Finally, the searched paths are smoothed by using greedy thinking for node selection, selecting key points, cutting redundant points of the path to make it more concise, and using cubic B-spline curves to make it smooth. In this paper, the superiority of the ACB-RRT* algorithm is verified through Matlab simulation experiments, and combine the improved algorithm with robot simulation platform practice, for path planning of the manipulator. Additionally, the algorithm will be further improved by incorporating machine learning to handle more welding robot path planning problems. Authors’ Contributions. Made substantial contributions to conception and design of the study and performed algorithm analysis and interpretation: Jiang JN, Zhi H, Tang XX, Cui C, and Wang XW. Declarations. Availability of Data and Materials. In this paper, algorithm and system are designed by the authors. The algorithm can be shared, but the developed system cannot be shared. Financial Support and Sponsorship. The authors appreciate the support of National key research and development program (2022YFB4602104), National Natural Science Foundation of China (62076095, 61973120). Conflicts of Interest. All authors declared that there are no conflicts of interest. Ethical Approval and Consent to Participate. Not applicable. Consent for Publication. All the authors have consented to the publication of this manuscript. Copyright. © The Author(s) 2023.

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148

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Author Index

A Aimin, Xu

23

C Can, Cui 107 Chen, Huabin 70 Chen, Nannan 42 Chen, Shanben 82 Chun, Yu 23 Cui, Can 126 D Ding, Yuhan

42

H Han, Qinglin 56 Hao, Lu 23 Heng, Zhi 107 Hou, Zhen 82 Hua, Xueming 42 Huijun, Pan 23 J Jiang, Junnan 126 Jin, Shi 23 Junnan, Jiang 107 Juntong, Xi 23 L Li, Wenhang 56 Liu, Erbin 82 Liu, Kai 70 Lu, Lin 56 Lu, Yang 70

M Ma, Jusha 42 Miethlich, Boris N Naikun, Wei P Pei, Yafei

3

23

42

Q Qian, Bin 42 R Rao, Mingzhen 70 Rundang, Yang 23 S Shen, Chen 42 Sheng, Gu 23 Shuai, Yang 23 Singgaran, Nachimani Charde T Tang, Guobao 82 Tang, Xiaoxiang 126 W Wang, Jianxin 56 Wang, Jiayou 56 Wang, Min 42 Wang, Xuewu 126 Wang, Zhichao 42

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. Chen et al. (Eds.): RWIA 2022, TIWM, pp. 149–150, 2024. https://doi.org/10.1007/978-981-99-9629-2

3

150

Wei, Yi 42 Wu, Guanzhi

Author Index

42

X Xiao, Runquan 82 Xiaomeng, Luo 23 Xiaoxiang, Tang 107 Xue, Zhanying 82 Xuewu, Wang 107

Y Yang, Feng 56 Yu, Rui 56

Z Zhi, Heng 126 Zhou, Hao 82 Zhu, Jie 56