Numerical Modelling and Optimization in Advanced Manufacturing Processes (Materials Forming, Machining and Tribology) 3031043006, 9783031043000

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Materials Forming, Machining and Tribology

Chander Prakash Sunpreet Singh Aminesh Basak J. Paulo Davim   Editors

Numerical Modelling and Optimization in Advanced Manufacturing Processes

Materials Forming, Machining and Tribology Series Editor J. Paulo Davim , Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

This series fosters information exchange and discussion on all aspects of materials forming, machining and tribology. This series focuses on materials forming and machining processes, namely, metal casting, rolling, forging, extrusion, drawing, sheet metal forming, microforming, hydroforming, thermoforming, incremental forming, joining, powder metallurgy and ceramics processing, shaping processes for plastics/composites, traditional machining (turning, drilling, miling, broaching, etc.), non-traditional machining (EDM, ECM, USM, LAM, etc.), grinding and others abrasive processes, hard part machining, high speed machining, high efficiency machining, micro and nanomachining, among others. The formability and machinability of all materials will be considered, including metals, polymers, ceramics, composites, biomaterials, nanomaterials, special materials, etc. The series covers the full range of tribological aspects such as surface integrity, friction and wear, lubrication and multiscale tribology including biomedical systems and manufacturing processes. It also covers modelling and optimization techniques applied in materials forming, machining and tribology. Contributions to this book series are welcome on all subjects of “green” materials forming, machining and tribology. To submit a proposal or request further information, please contact Dr. Mayra Castro, Publishing Editor Applied Sciences, via mayra.castro@springer. com or Professor J. Paulo Davim, Book Series Editor, via [email protected]

More information about this series at https://link.springer.com/bookseries/11181

Chander Prakash · Sunpreet Singh · Aminesh Basak · J. Paulo Davim Editors

Numerical Modelling and Optimization in Advanced Manufacturing Processes

Editors Chander Prakash School of Mechanical Engineering Lovely Professional University Punjab, India Aminesh Basak University of Adelaide Adelaide, SA, Australia

Sunpreet Singh National University of Singapore Singapore, Singapore J. Paulo Davim Department of Mechanical Engineering University of Aveiro Aveiro, Portugal

ISSN 2195-0911 ISSN 2195-092X (electronic) Materials Forming, Machining and Tribology ISBN 978-3-031-04300-0 ISBN 978-3-031-04301-7 (eBook) https://doi.org/10.1007/978-3-031-04301-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The proposed book “Numerical Modelling and Optimization in Advanced Manufacturing Processes” will offer the reader with comprehensive insights about the different kinds of numerical modeling and nature-inspire optimization methods in advanced manufacturing processes for understanding the process characteristics. Particular emphasis will be devoted to applications in non-conventional machining, nano-finishing, precision casting, porous biofabrication, three-dimensional printing, and micro/nano-scale modeling. The book will include the practical implications of empirical, analytical, and numerical models for predicting the vital output responses. Especially, emphasis will be given to Finite Element Methods (FEM) for understanding the design of novel highly complex engineering products, their performances, and behaviors under simulated processing conditions. Furthermore, the FEM is highly an efficient method of understanding the fatigue failure of biomedical components and neurosurgical bone grinding operations. The book, indeed, will provide valuable research reference for academic scholars, graduate students, and industrial engineers acting in the area of advanced manufacturing processes. Punjab, India Singapore, Singapore Adelaide, Australia Aveiro, Portugal

Chander Prakash Sunpreet Singh Aminesh Basak J. Paulo Davim

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Contents

Parametric Appraisal of Plastic Injection Moulding for Low Density Polyethylene (LDPE): A Novel Taguchi Based Honey Badger Algorithm and Capuchin Search Algorithm . . . . . . . . . . . . . . . . . . Siddharth Jeet, Abhishek Barua, Dilip Kumar Bagal, Swastik Pradhan, Surya Narayan Panda, and Siba Sankar Mahapatra A Comparison of Ferrofluid Flow Models for a Curved Rough Porous Circular Squeeze Film Considering Slip Velocity and Various Shapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jimit R. Patel and G. M. Deheri

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19

Simulation and Optimization Study on Polishing of Spherical Steel by Non-newtonian Fluids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Duc-Nam Nguyen, Ngoc Thoai Tran, and Thanh-Phong Dao

37

3D Modeling and Analysis of Femur Bone During Jogging and Stumbling Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imran Ahemad Khan

55

On Parametric Optimization of TSE for PVDF-Graphene-MnZnO Composite Based Filament Fabrication for 3D/4D Printing Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vinay Kumar, Rupinder Singh, and Inderpreet Singh Ahuja Multi-factor Optimization for Joining of Polylactic Acid-Hydroxyapatite-Chitosan Based Scaffolds by Rapid Joining Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Ranjan, R. Singh, and I. P. S. Ahuja

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93

Analysis of Dimensional Accuracy of Fused Filament Fabrication Parts Using Genetic Algorithm and Taguchi Analysis . . . . . . . . . . . . . . . . . 105 J. S. Chohan, R. Kumar, and S. Singh

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Contents

Introduction to Optimization in Manufacturing Operations . . . . . . . . . . . 115 Debojyoti Sarkar and Anupam Biswas Potential Application of CEM43 °C and Arrhenius Model in Neurosurgical Bone Grinding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Atul Babbar, Vivek Jain, Dheeraj Gupta, Chander Prakash, and Deepak Agrawal An Effective Selection of Laser Cutter Used in Stent Manufacturing Through Fuzzy TOPSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 M. Stephen, A. Felix, and A. Parthiban

About the Editors

Dr. Chander Prakash is a Professor in the School of Mechanical Engineering, Lovely Professional University, Jalandhar, India. His research areas are biomaterials, rapid prototyping and 3D printing, advanced manufacturing, modeling, simulation, and optimization. He has teaching experience and research expertise in titaniumand magnesium-based implants. Dr. Prakash has authored more than 180 research articles (among them >95 SCI indexed research article) in the journals, conference proceedings, and books (H-index 31, i10-index 84, Google Scholars citation 3185). In 2018 and 2019, he received the Research Excellence Award for publishing the highest number of publications at the University. He has edited 23 books and 3 authored books for various reputed publishers like Springer, Elsevier, CRC Press, and World Scientific. He is series editor of book “Sustainable Manufacturing Technologies: Additive, Subtractive, and Hybrid”, CRC Press Taylor & Francis, where more than 25 edited books were published by national and international researchers. He is serving editorial board member of peer-reviewer intranational journal “Cogent Engineering” and “Frontiers in Manufacturing Technology”. He is serving Guest Editor of peerreviewed SCI-indexed journals. Dr. Sunpreet Singh is a Researcher in NUS Nanoscience & Nanotechnology Initiative (NUSNNI). He received his Ph.D. in mechanical engineering from Guru Nanak Dev Engineering College, Ludhiana, India. His research areas are additive manufacturing and application of 3D printing for development of new biomaterials for clinical applications. Dr. Aminesh Basak is working as Dual Beam Engineer in the Division of Research and Innovation, The University of Adelaide, Australia. He received his Ph.D. in materials science and manufacturing. He is also FIB/TEM Specialist at Australian Institute for Machine Learning. His research areas are precision machining, development of metal matrix composites, tribological behaviors of engineering materials, and optimization of the manufacturing processes.

ix

x

About the Editors

Prof. J. Paulo Davim received his Ph.D. degree in mechanical engineering in 1997, M.Sc. degree in mechanical engineering (materials and manufacturing processes) in 1991, mechanical engineering degree (5 years) in 1986, from the University of Porto (FEUP), the aggregate title (full habilitation) from the University of Coimbra in 2005, and the D.Sc. (higher doctorate) from London Metropolitan University in 2013. He is Senior Chartered Engineer in the Portuguese Institution of Engineers with an MBA and specialist titles in engineering and industrial management as well as in metrology. He is also Eur Ing in FEANI, Brussels, and Fellow (FIET) of IET London. Currently, he is Professor in the Department of Mechanical Engineering of the University of Aveiro, Portugal. He is also distinguished as honorary professor in several universities/colleges. He has more than 30 years of teaching and research experience in manufacturing, materials, mechanical, and industrial engineering, with special emphasis in machining and tribology. He has also interest in management, engineering education, and higher education for sustainability.

Parametric Appraisal of Plastic Injection Moulding for Low Density Polyethylene (LDPE): A Novel Taguchi Based Honey Badger Algorithm and Capuchin Search Algorithm Siddharth Jeet, Abhishek Barua, Dilip Kumar Bagal, Swastik Pradhan, Surya Narayan Panda, and Siba Sankar Mahapatra Abstract In our quickly growing world, there is an increasing need for cheap, longlasting, and less hazardous materials for medical purposes. To meet their needs, medical goods ranging from intravenous fluid containers to medical syringes are manufactured utilizing a variety of thermoplastics and Plastic Injection Moulding (PIM). Even sophisticated profile mouldings, however, may suffer from dimensional inaccuracy. The present research contributes to a better knowledge of thermoplastics, namely Low-Density Polyethylene (LDPE) material moulding for medical syringe plungers utilizing injection moulding equipment. Eight input injection moulding parameters were examined to reduce the depth sink marks along with weight produced during injection moulding of thermoplastic LDPE material. The 27 trials were piloted in accord with Taguchi’s Design of Experiment, and the variables were optimized using the newly developed Honey Badger and Capuchin Search Algorithms, as well as analysis of variance, for determining the most dominating parameter. The cooling time and melt temperature of the plastic injection moulded part are the most significant factors influencing the sink-mark depth and weight of the part respectively, according to the Analysis of Variance (ANOVA) test. S. Jeet · A. Barua Department of Mechanical Engineering, Centre for Advanced Post Graduate Studies, BPUT, Rourkela, Odisha, India D. K. Bagal Department of Mechanical Engineering, Government College of Engineering, Kalahandi, Bhawanipatna, Odisha, India S. Pradhan (B) School of Mechanical Engineering, Lovely Professional University, Phagwara, Punjab, India e-mail: [email protected] S. N. Panda Department of Production Engineering, Birsa Institute of Technology, Sindri, Dhanbad, Jharkhand, India S. S. Mahapatra Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Prakash et al. (eds.), Numerical Modelling and Optimization in Advanced Manufacturing Processes, Materials Forming, Machining and Tribology, https://doi.org/10.1007/978-3-031-04301-7_1

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S. Jeet et al.

Keywords Low Density Polyethylene (LDPE) · Plastic injection moulding · Medical syringe · Taguchi method · Honey badger algorithm · Capuchin search algorithm

1 Introduction Medical materials that are cheap in cost, durable, and less dangerous are becoming more popular in today’s fast-paced environment. In order to meet their needs, medical goods ranging from intravenous fluid containers to medical syringes are manufactured using a variety of thermo-plastics through the use of Plastic Injection Moulding (PIM), which take in injecting molten plastic material into a mould to form the profile depicted in Fig. 1. They are thought to be less expensive than other techniques, such as the 3D printing process. The injection moulding method is extensively utilised in the manufacturing of medical goods, and a variety of thermoplastic polymers, such as ABS, Nylon, Polyethylene, Polypropylene, PVC, etc., are often employed in this process. One of the most common injection-molded component faults is the Sink-mark. Residual stresses induce this gate shrinkage, and if the gates are not correctly sealed after the injection cycle, it may lead to component failure. The product’s weight is an important consideration from the perspectives of both the economy and the environment. Lal and Vasudevan [1] investigated dimensional accuracy of injection moulded LDPE part using Taguchi method and reported cooling time was the most influential factor. Jaya et al. [2] investigated tensile characteristics injection moulded LDPE/RH Composite by employing Full Factorial Experiment and reported better tensile strength due to increase in filler loading. Roslan et al. [3] explored the dimensional and mechanical physiognomies of injection moulded LDPE using RSM and

Fig. 1 Plastic injection moulding process

Parametric Appraisal of Plastic Injection Moulding …

3

PSO and reported optimal results. Bakshi et al. [4] investigated accelerated weathering performance of injection moulded PP and LDPE composites reinforced with calcium rich waste resources and reported in enhancement in mechanical properties. Khan et al. [5] investigated mechanical characteristics of injection moulded LDPE-MWpart and reported enhancement in mechanical and thermal properties with addition of 50% of marble powder waste. Adeodu et al. [6] investigated microstructural and mechanical properties of injection moulded LDPE-CB and LDPE-Al part by employing RSM and reported significance of all process parameters. Jaya et al. [7] investigated impact characteristics injection moulded LDPE/RH Composite and reported better impact strength due to increase in filler loading. Jaya et al. [8] investigated flexural characteristics injection moulded LDPE/RH Composite and reported better flexural strength due to increase in filler loading. They also carried similar kind of work in Autudesk Moldflow Insight platform [9]. Ravikiran et al. [10] investigated the dimensional accuracy of the injection moulded PMMA part using Taguchi-WASPAS method and testified cooling time as the most important parameter. According to the findings of most studies, cooling time has the greatest impact on dimensional accuracy followed by other parameters. Additional research into further injection moulding aspects and their optimization utilising multi-objective optimization criteria is lacking, however. An eight-criteria approach was used for plastic injection moulding of medical syringe plungers made of commercial grade thermoplastic Low Density Polyethylene (LDPE) to reduce sink marks’ depth and weight while also increasing injection pressure and back pressure. This was done in accordance with Taguchi’s Design: 27 trials were piloted, and variables were optimised using the newly created Honey Badger Algorithm and Capuchin Search Algorithm and analysis of variance in order to find the most dominant one.

2 Experimental Procedure In this study, injection moulding was carried out using commercial-grade LDPE plastic. A medical injection plunger was used in the experiment. To formulate the tests under different conditions, we used a Maruti Engineers Fully Automatic Horizontal Molding Machine (Yudo brand: Model No. CW662), which was manufactured by Maruti Engineers. The tests make use of warming raw material, machine, and component data that has been entered into a computer-aided engineering (CAE) software. The trials were constructed using a Fully Automatic Horizontal Molding Machine, which is a full closed-loop control unit with a Hot Runner panel, and was used to create the components. The testing is carried out by feeding the machine with warmed raw material as well as the CAD model data for the component under consideration. Beginning with a 90 °C heat treatment for 2.5 h, the LDPE (Table 1) was dried to remove any remaining moisture before use. On had completed the process settings and had poured the melt into the machine. The mould chiller is adjusted to 185 °C for 45 s and then turned off. The component was manufactured in 75 s, according to the

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Table 1 Physical properties of Low-Density Polyethylene (LDPE) Commercial name

(SASOL™ LM2065)

Density (g/cm3 )

0.918

Molding shrinkage %

1.9

MFI

6.5 g/10 min

Tensile strength (MPa)

10.5

Hardness (HRB)

275

Tensile impact strength (MPa)

286

Flexural modulus (MPa)

252

Vicat softening point

0.961

Table 2 Experiment factors and their levels Code

Factor

Unit

L (1)

L (2)

L (3)

A

Injection pressure

MPa

35

42

46

B

Mold temperature

°C

50

60

70

C

Back pressure

MPa

7

10

12

D

Holding pressure

MPa

15

20

25

E

Melt temperature

°C

215

225

240

F

Ambient temperature

°C

20

25

30

G

Cooling time

Sec

15

20

25

H

Holding time

Sec

5

7

9

cycle time. The injected component was then inspected for shrinkage in the front view, short fill in the rib region, and flash in the locking rib in order to determine whether or not it was defective. Specifically, the present investigation seeks to minimise the appearance of weld lines on the component under consideration. The moulding parameters and their levels are shown in Table 2 of this document. A stable process state was established by experimenting with each process condition for 45 min during the trials. Figure 2 depicts a top sheath moulded specimen. Upon achieving the required injection pressure, all injections have been monitored as they transitioned from the injection phase to holding phase to ensure that the required injection pressure has been acquired. After 24 h of assembly, the parts are weighed and measured. A Nikon Epiphot 300/200 optical microscope was used to measure the sink-mark depth.

Parametric Appraisal of Plastic Injection Moulding …

5

Fig. 2 Moulded specimen of medical syringe plunger

3 Honey Badger Algorithm (HBA) It has been created by researchers from Egypt and Pakistan and is based on the hunting behaviour of honey badgers in nature [11]. As stated before, HBA is split into two stages, which are the “digging phase” and the “honey phase”, described in detail as follows: Step 1: Initialization phase. Initialize the number of honey badgers (population size N) and their corresponding locations based on Eq. (1): xi = lbi + ri × (ubi − lbi ), ri is a random number between 0 and 1

(1)

where x i is ith honey badger position referring to a candidate solution in a population of N, while lbi and ubi are respectively lower and upper bounds of the search domain. Step 2: Determining the degree of intensity (I). Intensity is proportional to the prey’s concentration strength and the distance between it and the honey badger. Ii is the prey’s scent strength; if the smell is strong, the motion will be rapid, and vice versa, as described by Eq. (2). Ii = r 2 ×

S , ri is a random number between 0 and 1 4π di2 S = (xi − xi+1 )2

(2)

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di = xprey − xi where S denotes the intensity of the source or the concentration of prey. di indicates the distance between the prey and the ith badger in Eq. (2). Step 3: Recalculate the density factor. The density factor (α) is used to regulate timevarying randomness in order to guarantee a seamless transition from exploration to exploitation. Utilize Eq. (3) to update a lowering factor α that lowers with iterations in order to reduce randomness:   −t , tmax = maximum number of iterations (3) α = C × exp tmax where C is a constant ≥1 (default = 2). Step 4: Getting away from the local optimum. This step, along with the two next ones, is used to exit local optima areas. In this context, the suggested method makes use of a flag F to change the direction of search, thus providing agents with a greater opportunity to thoroughly scan the search field. Step 5: Updating the locations of the agents. As previously stated, the process of updating the HBA position (xnew) is split into two phases: the “digging phase” and the “honey phase”. The following provides a more detailed explanation: Step 5 (i): The digging phase. During the digging phase, a honey badger executes an activity similar to that of a cardioid [2], as shown in Fig. 3. Equation (4) may be used to mimic the cardioid motion: d xnew =xprey + F × β × I × xprey + F × r3 × α × di ×|cos(2πr 4) × [1 − cos(2πr 5)]|

(4)

where x prey is location of the prey which is the best position discovered so far—global best position in other words. β ≥ 1 (default = 6) is ability of the honey badger to obtain food. di is distance between prey and the ith honey badger, see Eq. (2). r3, r4, and r5 are three distinct random integers between 0 and 1. F acts as the flag that changes search direction, it is calculated using Eq. (5):  F=

1 ifr6 ≤ 0.5 r6 is a random number between 0 and 1 −1 else,

(5)

In the digging phase, a honey badger significantly depends on scent intensity I of prey xprey, distance between the badger and prey di, and time-varying search influence factor α. Moreover, while digging activity, a badger may get any disruption F which enables it to locate even better prey position.

Parametric Appraisal of Plastic Injection Moulding …

7

Fig. 3 CSA flowchart

Step 5 (ii): Honey phase. The situation where a honey badger follows honey guide bird to approach beehive may be represented as Eq. (6): xnew = xprey + F × α × r7 × di , r6 is a random number between 0 and1

(6)

where x new denotes the honey badger’s new position and x prey denotes the prey location, and F and α are calculated using Eqs. (3) and (5), respectively. According to Eq. (6), a honey badger searches near to the prey site xprey discovered thus far, based on distance information di. At this step, the search is affected by time-varying search behaviour (α). Additionally, a honey badger may detect disruption F.

4 Capuchin Search Algorithm (CSA) It is based on the food hunting behaviour of South American Capuchin monkeys. During foraging, the capuchin leader and followers employ three strategies [12]. Group members follow the male alpha (global leader), who leads and controls the group and is in charge of finding food sources.

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• In certain capuchin species, the alphas (global leaders) are both male and female and command the group and are in charge of identifying food, with the rest of the group following the leaders. • While individuals seek food sources on their own, they tend to imitate the behaviour of their group members who are more active in this regard. Five different situations show how capuchins forage both inside and outside of the group to demonstrate this point: • When this happens, the group’s leaders start looking for food and determining where they are in respect to food sources. • Second, group members’ views about food sources shift in reaction to leaders’ positions and new sources being identified 2. • Those who are following the leaders get information on their group’s current status. • Fourth, in order to identify food sources, the leaders reevaluate their best-case scenarios. • Following a number of cycles in which the ranks of leaders and followers have not been changed, members of the group begin looking for food supplies in unexpected places. To find a food supply (i.e. the required solution), repeat the previous five scenarios endlessly. We may infer from the above description that the intelligent foraging behaviour of capuchins was the main driving force behind the creation of the metaheuristic algorithm detailed in the next section. Figure 3 depicts the CSA process flow.

5 Result and Discussions The exploratory findings in the Taguchi L27 array are given in Table 3, along with the sink-mark depth and weight as outcomes. Among the 27 trials, it should be noted that trial no. 16 has the smallest sink mark depth and trial no. 4 has the smallest weight. As shown by the primary impact plot (Fig. 4), A3B2C3D1E3F1G2H1 demonstrates the least factor setting by using Taguchi’s method for sink mark depth. Similarly, as shown in the problem description, A3B3C1D3E3F2G1H3 demonstrates the least factor setting using Taguchi’s method for weight which is shown in Fig. 5. As a result, these can be considered the optimal control factors for minimizing the depth and weight of sink marks on injection moulded LDPE parts. The R-squared value achieved was more than 98%, indicating the experiment’s importance in a field where error is very small.

Parametric Appraisal of Plastic Injection Moulding …

9

Table 3 L27 Experimental run with outputs Run No

A

B

1

35

50

2

35

50

3

35

4 5

C

D

E

F

G

H

Sink-marks depth (μm)

Weight (g)

7

15

215

20

15

5

0.1459

128.3

7

15

225

25

20

7

0.1456

141.0

50

7

15

240

30

25

9

0.1454

145.7

35

60

10

20

215

20

15

7

0.1461

125.0

35

60

10

20

225

25

20

9

0.1472

135.1

6

35

60

10

20

240

30

25

5

0.1482

137.0

7

35

70

12

25

215

20

15

9

0.1463

133.7

8

35

70

12

25

225

25

20

5

0.1487

141.0

9

35

70

12

25

240

30

25

7

0.1471

148.3

10

42

50

10

25

215

25

25

5

0.1454

139.4

11

42

50

10

25

225

30

15

7

0.1448

153.7

12

42

50

10

25

240

20

20

9

0.1490

145.9

13

42

60

12

15

215

25

25

7

0.1464

135.1

14

42

60

12

15

225

30

15

9

0.1471

146.9

15

42

60

12

15

240

20

20

5

0.1526

136.1

16

42

70

7

20

215

25

25

9

0.1442

142.5

17

42

70

7

20

225

30

15

5

0.1462

151.4

18

42

70

7

20

240

20

20

7

0.1491

146.1

19

46

50

12

20

215

30

20

5

0.1526

131.1

20

46

50

12

20

225

20

25

7

0.1489

143.1

21

46

50

12

20

240

25

15

9

0.1469

154.9

22

46

60

7

25

215

30

20

7

0.1512

139.4

23

46

60

7

25

225

20

25

9

0.1488

148.9

24

46

60

7

25

240

25

15

5

0.1481

157.7

25

46

70

10

15

215

30

20

9

0.1544

139.5

26

46

70

10

15

225

20

25

5

0.1504

146.1

27

46

70

10

15

240

25

15

7

0.1470

160.4

According to the ANOVA table (Table 4), cooling time for the moulded part is the most significant control factor, accounting for 37.72% of the variance in sink mark depth, while melt temperature for the moulded part accounts for 43.88% of the variance in weight reduction of the injection moulded LDPE component. Total relative significance was used to build the regression equation. This regression equation will be used as the fitness function in the Honey Badger and Capuchin Search Algorithms that were created using Matlab R2018a:

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S. Jeet et al.

Main Effects Plot for SN ratios Data Means

B

A

16.70

C

D

E

F

20

25 215 225 240 20

25

H

G

Mean of SN ratios

16.65

16.60

16.55

16.50

35

42

46

50

60

70

7

10

12

15

30

15

20

25

5

7

9

Signal-to-noise: Smaller is better

Fig. 4 Main effect plot for sink mark depth

Main Effects Plot for SN ratios Data Means

-42.5

C

B

A

D

E

F

20

25 215 225 240 20

25

H

G

-42.6

Mean of SN ratios

-42.7 -42.8 -42.9 -43.0 -43.1 -43.2 -43.3 -43.4 35

42

46

50

60

70

7

Signal-to-noise: Smaller is better

Fig. 5 Main effect plot for weight

10

12

15

30

15

20

25

5

7

9

2

2

2

2

E

F

G

H

26

2

D

Total

2

C

10

2

Residual error

2

B

DF

A

Source P

0.5770

0.0105

0.0280

0.2176

0.0800

0.0072

0.0064

0.0288

0.0275

0.1706

0.0010

0.0140

0.1088

0.0400

0.0036

0.0032

0.0144

0.0137

0.0853

1.82

4.86

37.72

13.87

1.25

1.12

4.99

4.77

29.58

13.34

103.46

38.04

3.43

3.07

13.69

13.09

81.12

0.002

0.000

0.000

0.073

0.091

0.001

0.002

0.000

7.3728

0.0015

0.1728

0.5805

0.6540

3.2354

0.3986

0.2013

0.4967

1.6317

Weight F

Adj SS

% Influence

Adj SS

Adj MS

Sink mark depth

Table 4 ANOVA for sink mark depth and weight

0.0001

0.0864

0.2902

0.3270

1.6177

0.1993

0.1006

0.2483

0.8158

Adj MS

0.02

2.34

7.87

8.87

43.88

5.41

2.73

6.74

22.13

% Influence

1285.87

649.41

1602.30

5263.23

557.58

1872.51

2109.54

10,436.24

F

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

P

Parametric Appraisal of Plastic Injection Moulding … 11

12

S. Jeet et al.

Minimize: Sink Mark Depth = − 0.8767 − 0.002919x1 + 0.01302x2 −0.01274x3 + 0.02572x4 +0.005375x5 − 0.01360x6 + 0.001132x7 +0.01164x8 + 0.000054x1 x1 −0.000008x2 x2 − 0.000013x3 x3 +0.000011x4 x4 + 0.000002x5 x5 +0.000079x6 x6 − 0.000136x7 x7 −0.000565x8 x8 − 0.000164x1 x8 −0.000063x2 x5 + 0.000064x2 x6 +0.000021x2 x7 + 0.000019x2 x8 −0.000039x3 x5 + 0.000599x3 x6 + 0.000327x3 x7 +0.000090x3 x8 − 0.000116x4 x5

(7)

Weight = − 1676 + 3.206x1 − 4.522x2 − 0.8296x3 −4.567x4 + 15.96x5 + 8.000x6 − 8.111x7 + 5.167x8 −0.02886x1 x1 + 0.03889x2 x2 + 0.007407x3 x3 + 0.1222x4 x4 −0.03393x5 x5 E ∗ E − 0.1511x6 x6 + 0.1956x7 x7 − 0.3194x8 x8

(8)

Subject to: 35 ≤x1 ≤ 46; 50 ≤ x2 ≤ 70; 7 ≤ x3 ≤ 12; 15 ≤ x4 ≤ 25; 215 ≤ x5 ≤ 240 20 ≤x6 ≤ 30; 15 ≤ x7 ≤ 25; 5 ≤x8 ≤ 9 According to the issue statement, the width and depth of weld lines and sink marks on injection moulded LDPE components must be reduced. To get optimum moulding parameters, certain fixed parameters were specified in the Honey Badger method with a total population size of 30 and a maximum iteration limit of 100. Figures 6 and 7 illustrates the fitness convergence curve when the HBA is used. Similarly, certain fixed parameters have been specified in the Capuchin Search method, with a total population size of 100 and a maximum iteration limit of 500. Figures 8 and 9 illustrates the fitness convergence curve when the CSA is used. Table 5 summarises the optimal factor setting together with projected fitness values. Unconfirmed optimization algorithm predictions were analysed to evaluate the predictive value’s importance. In order to determine the optimal factor setting for sink mark depth and weight reduction during LDPE injection moulding, the following information would be helpful: For injection moulded LDPE components, the HBA and CSA results are more accurate than the Taguchi method values, since sink marks depth and weight have been decreased by using the HBA and CSA factor settings

Parametric Appraisal of Plastic Injection Moulding …

Fig. 6 Convergence plot for sink mark width (HBA)

Fig. 7 Convergence plot for weight (HBA)

13

14

Fig. 8 Convergence plot for sink mark width (CSA)

Fig. 9 Convergence plot for weight (CSA)

S. Jeet et al.

Parametric Appraisal of Plastic Injection Moulding …

15

Table 5 Control parameter using different approaches Algorithms

Parameters

Factor setting for optimal Sink marks depth

Factor setting for optimal weight

Taguchi method

Injection pressure

46 MPa

46 MPa

Mold temperature

60 °C

70 °C

Back pressure

12 MPa

7 MPa

Holding pressure

15 MPa

25 MPa

Honey badger algorithm

Capuchin search algorithm

Melt temperature

240 °C

240 °C

Ambient temperature

20 °C

25 °C

Cooling time

20 s

15 s

Holding time

5s

9s

Predicted value of sink marks depth

0.1551 μm

–

Experimental value of sink marks depth

0.1540 μm

–

Predicted value of weight –

165.85 g

Experimental value of weight

–

158.58 g

Injection pressure

46 MPa

35 MPa

Mold temperature

70 °C

60 °C

Back pressure

12 MPa

12 MPa

Holding pressure

25 MPa

15 MPa

Melt temperature

240 °C

215 °C

Ambient temperature

20 °C

20 °C

Cooling time

15 s

20 s

Holding time

9s

5s

Predicted value of sink marks depth

0.0821 μm

–

Experimental value of sink marks depth

0.0811 μm

–

Predicted value of weight –

115.81 g

Experimental value of weight

–

115.50 g

Injection pressure

46 MPa

35 MPa

Mold temperature

70 °C

60 °C

Back pressure

12 MPa

12 MPa

Holding pressure

25 MPa

15 MPa (continued)

16

S. Jeet et al.

Table 5 (continued) Algorithms

Parameters

Factor setting for optimal Sink marks depth

Factor setting for optimal weight

Melt temperature

240 °C

215 °C

Ambient temperature

20 °C

20 °C

Cooling time

15 s

20 s

Holding time

9s

5s

Predicted value of sink marks depth

0.0821 μm

–

Experimental value of sink marks depth

0.0811 μm

–

Predicted value of weight – Experimental value of weight

114.81 g

–

rather of the Taguchi technique factor settings, rather than the Taguchi method values (Table 5). Early research had shown that cooling time was a significant factor in sink mark depth and melt temperature was a significant factor in weight. The results of this study were consistent with that previous research.

6 Conclusion Plastic injection moulding parameters were tested on the moulding of an LDPE medical syringe plunger in this experiment. To minimise sink-mark depth and weight during LDPE injection moulding, eight plastic injection moulding parameters were examined. Meta-heuristic optimization methods such as the Taguchi method in conjunction with Honey Badger and Capuchin Search Algorithms were used to enhance the control settings. For injection moulded LDPE components, using the HBA and CSA factor settings rather of the Taguchi technique factor settings decreased sink mark depth and weight. This is notable since the HBA and CSA results were more accurate than Taguchi method values. According to the ANOVA results, the most significant control factor is cooling time for the moulded part, which accounts for 37.72% of the variance in sink mark depth, and the most significant control factor for the injection moulded LDPE component is melt temperature, which accounts for 43.88% of the variance in weight reduction for the component. Because their output is more precise than that of the Taguchi method, the anticipated and experimental values produced by the Honey Badger and Capuchin search algorithms are nearly identical to those obtained by confirmatory testing. The Honey Badger and Capuchin search algorithms are also more precise than the Taguchi method. The

Parametric Appraisal of Plastic Injection Moulding …

17

optimal manufacturing of injection moulded LDPE components in companies that produce medical goods will be aided as a result of these efforts.

References 1. Lal, S.K., Vasudevan, H.: Optimization of injection moulding process parameters in the moulding of low density polyethylene (LDPE). Int. J. Eng. Res. Dev. 7(5), 35–39 (2013) 2. Jaya, H., Zulkepli, N.N., Omar, M.F., Abd Rahim, S.Z., Halim, K.A.A.: Optimization of injection moulding processing parameters for LDPE/RH composites tensile strength through full factorial experiment. In: IOP Conference Series: materials Science and Engineering, vol. 957, no. 1, p. 012039. IOP Publishing (2020) 3. Roslan, N., Abd Rahim, S.Z., Abdellah, A.E.H., Abdullah, M.M.A.B., Błoch, K., Pietrusiewicz, P., et al.: Optimisation of shrinkage and strength on thick plate part using recycled LDPE materials. Materials14(7), 1795 (2021) 4. Bakshi, P., Pappu, A., Bharti, D.K., Patidar, R.: Accelerated weathering performance of injection moulded PP and LDPE composites reinforced with calcium rich waste resources. Polym. Degrad. Stab. 192, 109694 (2021) 5. Khan, A., Patidar, R., Pappu, A.: Marble waste characterization and reinforcement in low density polyethylene composites via injection moulding: towards improved mechanical strength and thermal conductivity. Construct. Build. Mater. 269, 121229 (2021) 6. Adeodu, A.O., Kanakana-Katumba, M.G., Maladzhi, R.W., Daniyan, I.A.: Optimization of injection process parameters of plastic reinforced composites using response surface methodology and central composite design. In: Proceedings of the American Society for Composites—Thirty-Sixth Technical Conference on Composite Materials 7. Jaya, H., Noriman, N.Z., Rahim, S.Z.A., Omar, M.F., Hamzah, R., Dahham, O.S., Umar, M.U.: Impact strength of LDPE/RH composites for industrial injection moulded parts. In: AIP Conference Proceedings, vol. 2213, no. 1, p. 020256. AIP Publishing LLC (2020) 8. Jaya, H., Noriman, N.Z., Rahim, S.Z.A., Omar, M.F., Hamzah, R., Dahham, O.S., Umar, M.U.: Flexural properties of rice husk (Oryza sativa) reinforced low density polyethylene composites for industrial injection moulded parts. In: AIP Conference Proceedings, vol. 2213, no. 1, p. 020254. AIP Publishing LLC (2020) 9. Jaya, H., Zulkepli, N.N., Omar, M.F., Abd Rahman, S.Z., Halim, K.A.A.: Injection moulding recommended parameters simulation analysis of rice husk composite. In: IOP Conference Series: materials Science and Engineering, vol. 701, no. 1, p. 012048. IOP Publishing (2019) 10. Ravikiran, B., Pradhan, D.K., Jeet, S., Bagal, D.K., Barua, A., Nayak, S.: Parametric optimization of plastic injection moulding for FMCG polymer moulding (PMMA) using hybrid Taguchi-WASPAS-Ant Lion optimization algorithm. Mater. Today: Proc. (2021) 11. Hashim, F.A., Houssein, E.H., Hussain, K., Mabrouk, M.S., Al-Atabany, W.: Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math. Comput. Simul. 192, 84–110 (2022) 12. Braik, M., Sheta, A., Al-Hiary, H.: A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm. Neural Comput. Appl. 33(7), 2515–2547 (2021)

A Comparison of Ferrofluid Flow Models for a Curved Rough Porous Circular Squeeze Film Considering Slip Velocity and Various Shapes Jimit R. Patel and G. M. Deheri

Abstract The aim of this investigation is to compare the magnetic fluid flow models for the ferrofluid lubrication of porous rough curved circular squeeze films incorporating slip velocity, wherein various film shapes are considered. The random irregularity of the bearing faces is characterized by a stochastic random variable with non-zero skewness, mean and variance. The concerned Reynolds type relation is averaged under Christensen and Tonder’s stochastic modeling. Using Reynolds boundary conditions, this expression is solved for the pressure distribution. Then the load bearing is determined. Magnetic fluid lubrication tends to overcome roughness’ adverse effect as much as possible. This result remains superior in Shliomis (SH) model for almost all film shapes. The novelty of this examination reveals that if designed properly Shliomis model based ferrofluid lubrication may be adopted from industry point of view even for some film shapes Neuringer-Rosensweig (NR)’s model can be a suitable option. Keywords Circular bearing · Porosity · Roughness · Magnetic fluids · Slip velocity · Flow models · Load bearing capacity (LBC)

1 Introduction It is well known that squeeze film phenomenon occurs in clutch plates, car transmissions and households applications. Ferrofluids are developed by scattering the magnetized nano-particles in the base fluid. In the light of some crucial physical and chemical properties the ferrofluids are J. R. Patel (B) Department of Mathematical Sciences, P. D. Patel Institute of Applied Sciences, Charotar University of Science and Technology (CHARUSAT), CHARUSAT Campus, Changa, Anand, Gujarat 388 421, India e-mail: [email protected] G. M. Deheri Department of Mathematics, Sardar Patel University, Vallabh Vidyanagar, Anand, Gujarat 388 120, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Prakash et al. (eds.), Numerical Modelling and Optimization in Advanced Manufacturing Processes, Materials Forming, Machining and Tribology, https://doi.org/10.1007/978-3-031-04301-7_2

19

20

J. R. Patel and G. M. Deheri

found to be used in various engineering applications for instance imaging, ink-jet printer, cleaners, sealing etc. Several investigations are abound in the literature dealing with NeuringerRosensweig’s model based ferrofluid lubrication [1–10]. It has been concluded that Neuringer-Rosensweig’s model induce a rise in the load capacity. With the use of Maugin’s modification, the Neuringer-Rosensweig’s model [11] was modified to Jenkin’s model [12]. While Neuringer-Rosensweig’s model altered the pressure the Jenkin’s model changed the pressure and viscosity of the Ferrofluid. Jenkin’s model based Ferrofluid lubrication has been a matter of discussions in [1, 13–17]. But Shliomis’ [18] ferro-fluid flow model incorporated the influence of ferro particles’ rotation, concentration of volume and particles’ movements. Thus, this model was subjected to investigation in a number of articles [19–22]. It was noted that Shliomis model based ferro-fluid lubrication presented better scopes in countering the negative influence of roughness. In squeeze film bearing systems, slip is observed to be a significant factor affecting performance [23–28]. There is little comparison of the ferrofluid flow models on the squeeze film performance. Therefore, it was deemed proper to look into the comparison of models of NR, SH and Jenkin on a squeeze film performance in curved rough porous circular squeeze film with slip.

2 Analysis As can be seen in Fig. 1, the squeeze film circular bearing consists of two circular plates, each with same radius “a”. A normal uniform velocity h˙0 is applied between the upper and lower curved plates where h 0 is the central thickness of the film.

Fig. 1 Bearing’s formation (HH, HS and SS)

A Comparison of Ferrofluid Flow Models …

21

Surfaces presumed to be rough across the bearing are transverse. Due to [29], the lubricant film has a thickness h of the form h = h + hs

(1)

where h—mean thickness of film and h s —random roughness. h s remains described by the function of probability  f (h s ) =

35 32c

 1−

h 2s c2

3

, −c ≤ h s ≤ c

0, elsewhere

where c—extreme deviation, and the details for the measure of symmetry like α— mean, σ —standard deviation and ε—skewness [29]. In the discussions of [30, 31], opinions that the relations for upper plate lies along the surface depends on  [I ] z u = h 0

 1 − 1 ;0 ≤ r ≤ a 1 + βr

and  

[I I ] z u = h 0 sec βr 2 − 1 ; 0 ≤ r ≤ a and the expressions of lower plate specified by  [I ] zl = h 0

 1 − 1 ;0 ≤ r ≤ a 1 + γr

and  

[I I ] zl = h 0 sec γ r 2 − 1 ; 0 ≤ r ≤ a respectively, where β—the curvature of upper plate while γ —indicates the bottom plates’ curvature. The following film thicknesses; mooted by ([30, 31]) 

 1 1 − + 1 ;0 ≤ r ≤ a [H H ] h(r ) = h 0 1 + βr 1 + γr    1 − sec γ r 2 + 1 ; 0 ≤ r ≤ a [H S] h(r ) = h 0 1 + βr

22

J. R. Patel and G. M. Deheri

and   

[SS] h(r ) = h 0 sec βr 2 − sec γ r 2 + 1 ; 0 ≤ r ≤ a

(2)

are considered here. With laminar flow, the film of lubricant is assumed to be incompressible and isoviscous. A magnetic fluid is used as the lubricant. As part of an analysis of the stable flow of magnetic fluids in the occurrence of slow- changes in external ferro-fields, NR [11] has developed a simple flow model. A simple flow model to analyse the steady flow of ferro-fluids in the presence of gradually moving external magnetic fields was derived by NR [11]. The model comprised of the following equations  ρ(q∇)q = −∇ p + η∇ 2 q + μ0 M∇ H

(3)

∇q = 0

(4)

∇×H =0

(5)

M = μH

(6)

 ∇ H+M =0

(7)

where μ0 —free space’s permeability,H —applied field of magnetization, μ—ferrofield’s susceptibility and other standard parameters ρ, p, q and η. For further one can turn to [30, 32]. Using Eqs. (4)–(7), (2), (4) and (5) Eq. (3) becomes μ0 μ 2 M + η∇ 2 q ρ(q∇)q = −∇ p − 2 

As a result, NR’s modified Reynolds equation determines the film pressure as    μ0 μ 2 d 1 d = 12ηh˙0 h 3r p− M r dr dr 2

(8)

In 1972, Shliomis discovered ferro-particles in a ferro-fluid could relax in two ways in response to changes in ferro-fields. Particles in the fluid rotated one way, while magnetic-moments within the particles rotated another way. The revised Reynolds type equation for the model of SH emerges from [16, 30], becomes  1 d 3 dp h r = 12ηa h˙0 = 12η(1 + τ )h˙0 r dr dr

(9)

A Comparison of Ferrofluid Flow Models …

23

Jenkins reformed the tactic of NR’s model to suggest framework for the ferrofluid’s flow. According to Maugin’s amendment, steady flow’s expression can be determined by ([12, 13, 17]). 



ρ A2 ∇× ρ(q · ∇)q = −∇ p + η∇ q + μ0 M · ∇ H + 2 2

  M  × (∇ × q) × M M (10)

composed with Eqs. (4)–(7), A—constant of material. The Eqs. (3) and (10) clearly show that Jenkins framework is an extension of NR’s model with a term

    ρJ2 M  H  ρ A2 μ ∇× × (∇ × q) × M = ∇× × (∇ × q) × H (11) 2 M 2 H which indicates an improvement in fluid’s property. A modified Reynolds type equation for Jenkins takes on the form ⎞ ⎛  1 d ⎝ h3 μ0 μ 2 ⎠ d  r p− H = 12ηh˙0 ρ A2 μH r dr dr 2 1−

(12)

2η

when considering [17, 30]. The reformed Reynolds equation that governs the pressure distribution for NR, SH and Jenkins models, respectively, is derived from the hydrodynamic liquid lubrication assumptions ([30–32]) and stochastic process of [29],    1 d d μ0 μ 2 M = 12ηh˙0 g(h)r p− r dr dr 2  dp 1 d g(h)r = 12η(1 + τ )h˙0 r dr dr

(13) (14)

and ⎛ 1 d ⎝ g(h) d  r ρ A2 μH r dr dr 1− 2η



⎞ μ0 μ 2 ⎠ p− M = 12ηh˙0 2

(15)

where  4 + sh  2 2 2 3 g(h) = h + 3h α + 3 σ + α h + 3σ α + α + ε + 12φ H0 2 + sh 

3

2

24

J. R. Patel and G. M. Deheri

φ—permeability and H0 —thickness of the porous facing. Non-dimensional measures include those listed below, h r σ α ε h3 p , R = , P = − 02 , σ˜ = , α = , ε = 3 , ˙ h0 a h h h ηa h 0 0 0 0 √ kμ0 μh 30 2 ρ A2 μ ka φH (a − r ) ∗ ,μ = − , s = sh 0 , ψ = 3 (16) ,A = M 2 = kr 2 a 2η h0 ηh˙0 h=

and B = βa, C = γ a(for HH), B = βa, C = γ a 2 (for HS) and B = βa 2 , C = γ a 2 (for SS). The Reynolds boundary conditions are  P(1) = 0,

dP dR

=0

(17)

R=0

Using the dimensionless measures (16), the Eqs. (13–15) transform correspondingly into,    μ∗ 2 d 1 d g(h)R P− R (1 − R) = −12 R dR dR 2  1 d dP g(h)R = −12(1 + τ ) R dR dR

(18) (19)

and ⎞ 1 d ⎝ g(h) μ 2 d  R P− R (1 − R) ⎠ = −12 2 √ R dR d R 2 1− A R 1− R ⎛



∗

where    4 + sh  2   3 2 2 2 3 g h = h + 3h α + 3 σ + α h + 3σ α + α + ε + 12ψ 2 + sh and  1 1 − + 1 ;0 ≤ r ≤ a [H H ] h(r ) = 1 + BR 1 + CR    1 − sec C R 2 + 1 ; 0 ≤ r ≤ a [H S] h(r ) = 1 + BR 

(20)

A Comparison of Ferrofluid Flow Models …

25

and 

  [SS] h(r ) = sec B R 2 − sec C R 2 + 1 ; 0 ≤ r ≤ a The dimensionless pressures for the NR, SH, and Jenkins models can be calculated by solving Eqs. (18–20) and boundary conditions (16), R R μ∗ 2 R (1 − R) − 6 P= dR 2 g(h)

(21)

1

 P = −6(1 + τ )

R 1

R  dR g h

(22)

and R  R  μ∗ 2 2 √ 1 − A R 1 − R dR R (1 − R) − 6 P= 2 g(h)

(23)

1

Therefore, the dimensionless load-capacity is as follows: h 30 w = W =− 2π ηa 4 h˙0 W =−

h 30 w= 2π ηa 4 h˙0

1

μ∗ R Pd R = +3 40

0

1 0

1

1 R Pd R = 3(1 + τ )

0

0

R3  d R g h

(24)

R3  dR g h

(25)

and h 30 w = W =− 2π ηa 4 h˙0

1 0

μ∗ +3 R Pd R = 40

1 0

 R3  2 √  1 − A R 1 − R dR g h (26)

26

J. R. Patel and G. M. Deheri

3 Results and Discussion In comparison with the traditional lubricant-based bearings, Eqs. (24–26) show that the LBC increases. It is possible that this is because the viscosity increases. According to these expressions, the LBC of these systems increases with increasing magnetization, as they are linear with respect to magnetization. Figures 2, 3, 4, 5, 6 and 7 suggest that the magnetic outcome turns out to be best in Shliomis model.

Fig. 2 W in light of μ∗ /τ and B

Fig. 3 W in light of μ∗ /τ and C

A Comparison of Ferrofluid Flow Models …

27

Fig. 4 W in light of μ∗ /τ and 1/s

Fig. 5 W in light of μ∗ /τ and ε

The opposite nature of the effects of curvature parameters is exhibited in Figs. 8, 9, 10, 11 and 12. The impact of slip velocity is to lower the LBC, as can be seen from Figs. 13 and 14. In addition, the slip influence is felt nominally in the case of Shliomis model based lubrication. The influence of transverse roughness is displayed in Figs. 15, 16 and 17. This effect turns out to be a little unfavorable but the Shliomis model offers to control the situation up to considerable extent.

28

J. R. Patel and G. M. Deheri

Fig. 6 W in light of μ∗ /τ and α

Fig. 7 W in light of B and ψ

The graphical representations offer the following indications: 1. 2. 3.

The lower plate’s shape influences the most in the Shliomis model. The combined effect of slip and porosity is to bring down the load carrying capacity. All the three models enhance the bearing performance characteristics for all the shapes. But the Shliomis model turns out to be a little above in comparison with the other two models so far as roughness is concerned. However, the effects of

A Comparison of Ferrofluid Flow Models …

29

Fig. 8 W in light of B and σ˜

Fig. 9 W in light of B and ε

4.

5.

skewness and variance are alike for Neuringer-Rosensweig’s model and Jenkin’s model. The combined positive impact of negatively skewed roughness and variance (−ve) tends to augment the bearing performance for all the three models and all shapes when slip is at reduced level. In addition, the Shliomis model goes ahead of the other two models in reducing the negative effect of porosity, slip and roughness for all film shapes.

30

J. R. Patel and G. M. Deheri

Fig. 10 W in light of B and α

Fig. 11 W in light of B and ψ

6. 7.

8.

Up to some extent, the impact of standard deviation remains more prominent in Neuringer-Rosensweig’s model when compared with Shliomis model. When both the plates are taken to be of hyperbolic shapes the situation remains more effective for all the three fluid flow model, in mitigating the effect of roughness. It is appealing to note that the bearing can support some amount of load even when there is no flow which is unheard of in traditional lubricant.

A Comparison of Ferrofluid Flow Models …

31

Fig. 12 W in light of C and 1/s

Fig. 13 W in light of 1/s and α

9.

Exclusively, for the combined effect of porosity and slip the NeuringerRosensweig’s model may be adopted when the roughness is at lower level.

4 Conclusion Although, Neuringer-Rosensweig’s model may be adopted to reduce the effect of roughness and porosity when the slip is at low level, the Shliomis model registers an augmented performance even when the slip is a little bit more for all film shapes. Thus, for a bearing design intending long run the Shliomis model might be an option

32

J. R. Patel and G. M. Deheri

Fig. 14 W in light of 1/s and ψ

Fig. 15 W in light of σ˜ and ψ

for moderate to higher loads irrespective of slip effect. Besides the load upheld by the bearing system remains higher due to Shliomis model even in the absence of flow. Lastly, even if the Shliomis model is in use, attention should be paid to roughness when creating a bearing system.

A Comparison of Ferrofluid Flow Models …

33

Fig. 16 W in light of ε and α

Fig. 17 W in light of ε and ψ

References 1. Agrawal, V.K.: Magnetic-fluid-based porous inclined slider bearing. Wear 107(2), 133–139 (1986) 2. Bhat, M.V., Deheri, G.M.: Porous slider bearing with squeeze film formed by a magnetic fluid. Pure Appl. Math. Sci. 39(1–2), 39–43 (1995) 3. Shah, R.C., Bhat, M.V.: Squeeze film based on magnetic fluid in curved porous rotating circular plates. J. Magn. Magn. Mater. 208(1), 115–119 (2000) 4. Deheri, G.M., Andharia, P.I., Patel, R.M.: Transversely rough slider bearings with squeeze film formed by a magnetic fluid. Int. J. Appl. Mech. Eng. 10(1), 53–76 (2005)

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5. Nada, G.S., Osman, T.A.: Static performance of finite hydrodynamic journal bearings lubricated by magnetic fluids with couple stresses. Tribol. Lett. 27(3), 261–268 (2007) 6. Sinha, P., Adamu, G.: THD analysis for slider bearing with roughness: special reference to load generation in parallel sliders. Acta Mech. 207, 11–27 (2009) 7. Deheri, G.M., Patel, J.R.: Magnetic fluid based squeeze film in a rough porous parallel plate slider bearing. Annals J. Egg. 9(3), 443–463 (2011) 8. Patel, J.R., Deheri, G.M.: A comparison of porous structures on the performance of a magnetic fluid based rough short bearing. Trib. Ind. 35(3), 177–189 (2013) 9. Patel, N.S., Vakharia, D.P., Deheri, G.M., Patel, H.C.: Experimental performance analysis of ferrofluid based hydrodynamicjournal bearing with different combination of materials. Wear 376–377, 1877–1884 (2017) 10. Patel, J.R., Deheri, G.M.: A comparison of magnetic fluid flow models on the behavior of a ferrofluid squeeze film in curved rough porous circular plates considering slip velocity. Iranian J. Sci. Technol. Trans. A: Sci. 42(4), 2053–2061 (2018) 11. Neuringer, J.L., Rosensweig, R.E.: Magnetic fluids, magnetic fluid. Phys. Fluids 7(12), 1927 (1964) 12. Jenkins, J.T.: A theory of magnetic fluids. Arch. Ration. Mech. Anal. 46, 42–60 (1972) 13. Ram, P., Verma, P.D.S.: Ferrofluid lubrication in porous inclined slider bearing. Indian J. Pure Appl. Math. 30(12), 1273–1281 (1999) 14. Shah, R.C., Bhat, M.V.: Ferrofluid lubrication in porous inclined slider bearing with velocity slip. Int. J. Mech. Sci. 44(12), 2495–2502 (2002) 15. Ahmad, N., Singh, J.P.: Magnetic fluid lubrication of porous pivoted slider bearing with slip velocity. Proc. Instit. Mech. Eng. Part J: J. Eng. Tribol. 221(5), 609–613 (2007) 16. Patel, J.R., Deheri, G.: Effect of various porous structures on the Shliomis model based ferrofluid lubrication of the film squeezed between rotating rough curved circular plates. Facta Univ. Ser.: Mech. Eng. 12(3), 305–323 (2014) 17. Patel, J.R., Deheri, G.M.: Performance of a ferrofluid based rough parallel plate slider bearing: a comparison of three magnetic fluid flow models. Adv. Tribol. 2016, Article ID 8197160 (2016) 18. Shliomis, M.I.: Effective viscosity of magnetic suspensions. Sov. Phys. JETP 34(6), 1291–1294 (1972) 19. Kumar, D., Sinha, P., Chandra, P.: Ferrofluid squeeze film for spherical and comical bearings. Int. J. Eng. Sci. 30(5), 645–656 (1992) 20. Singh, U.P., Gupta, R.S.: Dynamic performance characteristics of a curved slider bearing operating with ferrofluid. Adv. Tribol. 2012 (2012). Article Id 278723 21. Lin, J.R.: Fluid inertia effects in ferrofluid squeeze film between a sphere and a plate. Appl. Math. Model. 37(7), 5528–5535 (2013) 22. Patel, J.R., Deheri, G.: Shliomis model-based magnetic squeeze film in rotating rough curved circular plates: a comparison of two different porous structures. Int. J. Comput. Mater. Sci. Sur. Eng. 6(1), 29–49 (2014) 23. Salant, R.F., Fortier, A.E.: Numerical analysis of a slider bearing with a heterogeneous slip/noslip surface. Trib. Tran. 47(3), 328–334 (2004) 24. Wu, C.W., Ma, G.J., Zhou, P., Wu, C.D.: Low friction and high load support capacity of slider bearing with a mixed slip surface. J. Trib. 128(4), 904–907 (2006) 25. Oladeinde, M.H., Akpobi, J.A.: A study of load capacity of finite slider bearings with slip surfaces and Stokesian couple stress fluids. Int. J. Eng. Rea. Afri. 1(2), 57–66 (2010) 26. Singh, J.P., Ahmad, N.: Analysis of a porous-inclined slider bearing lubricated with magnetic fluid considering thermal effects with slip velocity. J. Braz. Soc. Mech. Sci. Eng. 33(3), 351–356 (2011) 27. Patel, N.D., Deheri, G.M.: A ferrofluid lubrication of a rough, porous, inclined slider bearing with slip velocity. J. Mech. Eng. Tech. 4(1), 15–34 (2012) 28. Rao, R.R., Gouthami, K., Kumar, J.V.: Effect of velocity-slip and viscosity variation in squeeze film lubrication of two circular plates. Trib. Ind. 35(1), 51–60 (2013)

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29. Christensen, H., Tonder, K.C.: The hydrodynamic lubrication of rough bearing surfaces of finite width. In: ASME-ASLE Lubrication Conference (1970), Paper no.70-lub-7 30. Bhat, M.V.: Lubrication with a Magnetic Fluid, Team Spirit (India) Pvt. Ltd, (2003) 31. Abhangi, N.D., Deheri, G.M.: Magnetic fluid based squeeze film behavior between curved circular plates and surface roughness effect. FDMP 5(3), 245–259 (2009) 32. Prajapati, B.L.: On certain theoretical studies in hydrodynamic and electro-magneto hydrodynamic lubrication. Ph.D. thesis. S.P. University, VallabhVidya-Nagar (1995)

Simulation and Optimization Study on Polishing of Spherical Steel by Non-newtonian Fluids Duc-Nam Nguyen, Ngoc Thoai Tran, and Thanh-Phong Dao

Abstract The spherical surfaces will become important in the areas of industrial production such as jet engines, optical lenses, mould techniques, artificial knee joints, and bearings. These surfaces require high surface quality and form accuracy for the application process. To improve machining quality, the non-Newtonian fluid polishing methods are used to polish the complex surfaces. The process utilizing the shear thickening effect of non-Newtonian fluid based on abrasive slurries to achieve low surface roughness of product. During machining, the main factors affecting the surface quality of workpieces and material removal rate include polishing angles (A), work gap between the workpiece and bottom of the polishing tank (G), and tank velocity (V ). The effects of these factors on the machining process are simulated by ANSYS software. The cutting pressure (P) and polishing velocity (V m ) will be discussed and analyzed in this chapter. Finally, the multi-responses optimization is utilized to optimize the maximum pressures and polishing velocity on the workpiece surfaces in machining process. Based on the simulation and optimization results, the best machining parameters were established for improving the maximum pressure and polishing velocity which is distributed on the workpiece surface in polishing process. Moreover, the optimal parameters that are determined during the simulation will be a good support for establishing the conditions for the next experiment process. It was found that the optimal parameters for polishing spherical steel with better pressure and polishing velocity are polishing angle of 13.34°, work gap of 14.25 mm and tank velocity of 2.2 m/s.

D.-N. Nguyen (B) · N. T. Tran Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam e-mail: [email protected] T.-P. Dao Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Prakash et al. (eds.), Numerical Modelling and Optimization in Advanced Manufacturing Processes, Materials Forming, Machining and Tribology, https://doi.org/10.1007/978-3-031-04301-7_3

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Keywords Shear thickening polishing (STP) · Non-Newtonian fluid · Spherical surface · Polishing angle · Tank velocity · Viscosity · Optimization

1 Introduction The rapid development in modern optic, aerospace, biomedical technology and bearings have made spherical surfaces, increasingly demanded recently. These surfaces require a very high surface quality and shape accuracy to improve the efficiency of the working process. There are many conventional and non-conventional processes that applied for grinding the workpieces in various industrial application [1–5]. In this method, a small grinding disc is used to polish the surface of the workpieces. However, the surface roughness, form accuracy and surface integrity of the workpieces are limited in the grinding process. In addition, the lapping and polishing process with double plates were used to increase the surface quality of workpiece [6–9]. In this method, the relative motion between the double plates and the workpieces was affected by slurries under lapping load. The results indicated that the surface roughness of the workpieces were significantly improved. However, this methods only processes some simple surface of the workpieces. The elastic deformation machining method has been applied in machining the large spherical surfaces. The method allows the lapping and polishing of spherical surfaces using double polishing plates through on the elastic deformation of the workpieces [10–13]. The polished surface quality is changed, and surface roughness can be reduced. But, the form accuracy of product is greatly affected by the elasticity of the material during the machining process. For this reason, the surface quality and form accuracy of workpieces does not meet the product specifications. Currently, there are many different polishing technologies that have been studied and developed to improve surface quality of workpieces such as magneto-rheological finishing (MRF), elastic emission processing (EEM), electrolytic polishing (EP), bonnet tool polishing (BTP), magnetic abrasive finishing (MAF), et al. In the MRF process, the polishing slurry contains magneto-rheological (MR) fluid combine with abrasive particles [14–18]. As the results, the curved surface can be processed under magnetic field applied. However, this MR fluid has a high cost, which limits its wide application in pratical manufacturing. EEM is a polishing process which atomic abrasive slurry is used to remove material from a substrate [19, 20]. Therefore, the subsurface of workpiece is upgraded and avoid damage in machining process. In the EP process, a catalytic material is placed between the electrode and the polished material [21, 22]. Thus, catalytically generated ions are supplied from an external field to generate material removal on the workpiece surfaces. The bonnet tool polishing provides an enabling and effective approach for generating microstructured surfaces [23–26]. The workpieces surface can be automatically polished by the adaptation of the BTP process. In the MAF process, a relative motion between the magnetic abrasive and the workpiece is generated to polish the workpiece [27–30]. The complex curved surfaces can be machined easily when using this process.

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In order to improve the machining performance process and surface quality of product, the shear thickening polishing (STP) methods is carried out and applied to polish the complex surfaces. STP is known as a finishing method with advantages such as high efficiency, good surface quality, and good machinability for many different shapes. The shear thickening fluid is a combination of non-Newtonian fluid and abrasives. It will become a solid due to its viscosity increased with the increase of the shear rate [31]. There are many engineering fields that applied of the STF method including body armor [32], damping devices [33], and finishing process [34–36]. In addition, there are many complex surfaces such as spherical surfaces [37], gear surfaces [38], and cutting edge of cemented carbide insert [39] were polished by using STF technology. The scratches on the workpiece surfaces were removed by the pressure of the polishing fluid. Therefore, the surface roughness Ra/Rz of the workpiece is reduced rapidly. In the STP process, the machining conditions such as tank velocity, polishing viscosity, polishing angles, working gap of workpiece greatly affect the cutting efficiency, pressure value and quality of machined surfaces [40, 41]. Therefore, the optimization of these parameters is one of the important problem to determine the best machining conditions. In the optimization process, the Taguchi method has been applied to analyze and design of the machining parameters [42–45]. In addition, the mathematical models of surface roughness using response surface method were also used to find out the optimum equation of machining parameters [46–49]. In this chapter, the influences of machining parameters such as polishing angles (A), work gap between the workpiece and bottom of the polishing tank (G), and tank velocity (V ) on the spherical SKD11 steel are simulated by ANSYS fluent software. According to the simulation results, the cutting pressure and polishing velocity will be carried out and analyzed because they greatly affect the surface quality and the material removed rate during the machining process. In addition, the multi-responses optimization is utilized to optimize the maximum pressures and polishing velocity on the workpiece surfaces in STP process. As a results, the best machining parameters were established for improving the maximum pressure and polishing velocity which is distributed on the workpiece surface. Finally, the optimal parameters that are determined during the simulation will be a good support for establishing the conditions for the next experiment progress.

2 Shear Thickening Polishing Process 2.1 Principle of STP Method for the Spherical Surface Shearing thickening polishing is one of a machining method that uses a nonNewtonian fluids to generate the cutting pressure zone. The STP slurry is a mixture of abrasive particles, multi-hydroxyl polymer, DI water, and dispersing agent. They are stirred in a suitable time so that the ingredients are mixed and uniformly dispersed

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Fig. 1 Principle of STP process

[50]. The principle of STP process for machining spherical workpiece surface is illustrated in Fig. 1. When the STP slurry is rotated and affected to the workpiece, the shear thickening area will be generated. At that time, the abrasive particles and solid colloidal particles are bonded and clustered near the surface of the workpiece. In the shear thickening area, the viscosity of the STP slurry will be increased to maintain the material removal capacity of the abrasives. In addition, the shearing force on the abrasive particles is also greatly increased. As a results, scratches on the workpiece surface are removed by micro-abrasive polishing tool that is formed in the cutting area. When the STP slurry flows through the cutting area, the shearing force is reduced, the particles in the STP slurry are also separated and returned to its original state.

2.2 Fluid Simulation on Spherical Surface in STP Process In STP process of the spherical surface, the polishing fluid only touches a specified area on the workpiece surface. In order to machining the entire of the workpiece surface, the workpiece need to be rotated and the polishing angle is changed in accordance with the dimensions of the workpiece. In addition, the tank velocity, the polishing slurry viscosity and the work gap between workpiece and polishing tank will influence the distribution of pressure and fluid velocity on the workpiece surface [38, 39]. These technological parameters have a great impact on the surface quality and the material removal rate of workpiece. In the STP slurry flow, the effect of shear thickening occurs near the workpiece surface area. Therefore, the influence of viscosity is mostly expressed in the shear elastic layer near the wall of workpiece surface. The equation of STP slurry flow obeys the non-Newtonian power law, which are described as follows [51]: τ = η · γ˙ n when (n > 1)

(1)

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where τ is shear strees, η is the viscosity, γ˙ is the shear rate and n is viscosity index. In addition, the viscosity η of STP slurry can be written as [51] η = K · |γ˙ |n−1 when (n > 1)

(2)

where K is consistency index.

2.2.1

Simulation Conditions

In simulation process, the workpieces to be machined is SKD11 steel spherical surface with a diameter of 40 mm and height of 20 mm. Three parameters were selected as main design factors including the tank velocity (V ), work gap between the workpiece and the bottom of polishing tank (G), and polishing angles (A), that greatly affects the distributed pressure and material removal rate on the workpiece surface. The polishing angle of 0° and 40° were selected as level 1 and level 5 of the control factor 1, respectively. The work gap of 10 and 30 mm were chosen as level 1 and level 5 of the control factor 2. In addition, the tank velocity of 1.0 and 2.2 m/s were also selected as level 1 and level 5 of the control factor 3. The three process control factors and their levels are summarized in Table 1. A simulation model is designed on Autodesk Inventor and imported into Ansys software, as shown in Fig. 2. The diameter of the polishing tank, and distance between the workpiece and the inner boundary of the polishing tank are 300 mm, and 10 mm, respectively. In the simulation process, the STP polishing slurry is established according to nonNewtonian power law. Based on the previous studies, the consistency and viscosity index of polishing slurry were set to 0.62 and 1.5, respectively [50]. For boundary conditions, there is no relative movement on all the remaining walls. The simulation model is meshed with a total of 121,500 nodes and 618,615 elements. The geometry of meshing model is presented in the Fig. 3. Table 1 Design factors and their level Design variable

Factors

Level 1

Level 2 160 0.8 900

Level 3

Level 4

Level 5

Polishing angle: A (deg.)

A

0

10

20

30

40

Work gap: G (mm)

B

10

15

20

25

30

Tank velocity: V (m/s)

C

1.0

1.3

1.6

1.9

2.2

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Fig. 2 The solid model in simulation process

Fig. 3 Geometry of element meshing model

2.2.2

Simulation Results

It is well known that the L25 orthogonal array was applied in this study because it is appropriate for the three design control factors with five levels. The simulated data for the maximum pressures (P) and maximum polishing velocity (V m ) at the workpiece surface were shown in Table 2. The distributed pressure P and polishing velocity V m on the workpiece surface under different polishing parameters can be determined from the simulation results.

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Table 2 Plan of simulations and their responses No

Polishing angles A (°)

Work gap G (mm)

Tank velocity V (m/s)

Max. pressure P (Pa)

Max. velocity V m (m/s)

1

0

10

1.0

3575

2

0

15

1.3

7631

1.371 1.712

3

0

20

1.6

11,350

2.131

4

0

25

1.9

15,550

2.286

5

0

30

2.2

17,410

2.759

6

10

10

1.3

5502

1.736

7

10

15

1.6

9987

2.137

8

10

20

1.9

14,940

2.499

9

10

25

2.2

17,450

2.882

10

10

30

1.0

2861

1.335

11

20

10

1.6

10,670

2.119

12

20

15

1.9

13,980

2.510

13

20

20

2.2

17,490

2.882

14

20

25

1.0

3456

1.350

15

20

30

1.3

5476

1.724

16

30

10

1.9

15,060

2.511

17

30

15

2.2

15,680

2.900

18

30

20

1.0

3876

1.386

19

30

25

1.3

5827

1.727

20

30

30

1.6

7296

2.128

21

40

10

2.2

14,830

2.880

22

40

15

1.0

3623

1.411

23

40

20

1.3

6947

1.770

24

40

25

1.6

7242

2.132

25

40

30

1.9

9346

2.508

Figure 4 shows the distribution of the pressure on the spherical surface of the workpiece at different design control factors. The results indicated that the distributed pressure area is changed on the different polishing parameters. In addition, the velocity of STP slurry are also affected under different machining parameters, as shown in Fig. 5.

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Fig. 4 Distributed pressure on spherical surface of workpiece

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Fig. 5 Velocity of STP slurry under different machining parameters

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3 Optimization Results and Discussion According to the simulation results, the good surface quality and material removal rate can be achieved at the zone of maximum pressure and polishing velocity, respectively. Therefore, optimizing the polishing parameters to determine the maximum pressure and polishing velocity values on the workpiece surface are necessary.

3.1 Regression Model Three polishing parameters were selected as main design factors including the tank velocity (V), work gap between the workpiece and the bottom of polishing tank (G), and polishing angles (A), that greatly affects the distributed pressure and polishing velocity on the workpiece surface. As shown in Table 2, a regression equation of the pressure was established by using the response surface methodology in Minitab software 19 [47]. The regression equation for maximum pressure was given below P = −13585 + 179.8A + 466G + 13243V −0.802 A2 − 10.94G 2 − 389V 2

(3)

−5.49A ∗ G − 61.3A ∗ V where P represents the pressure on the workpiece surface. The coefficients for the regression equation of the pressure P was presented in Table 3. As a results, the R2 of 98.37% and the Predicted R2 of 95.13% are in equitable agreement with the Adjusted R2 of 97.56% under CI of 95%; that is to imply, the disparity is less than 0.02. This proves that the full quadratic equation is an appropriate regression model for the pressure P. Table 3 Coefficients of regression equation for pressure P Term

Coef

SE Coef

95% CI

T-Value

P-Value

Constant

10,660

412

A

−1202

225

(9786, 11,534)

25.86

A1 > A3 > A4 . To tackle vagueness in decision making and ambiguity in the problem’s information, it is necessary to obtain a model for these phenomena. MADM technique is one of the method to select the better alternative from many alternatives. By utilizing Fuzzy TOPSIS method, the Fiber laser is selected among different lasers such as Flash lamp-pumped Nd: YAG laser, Fiber laser, Disk laser, Nanosecond laser, Picosecond laser, Femtosecond laser.

5 Conclusion The Fuzzy TOPSIS method using trapezoidal fuzzy number is applied in laser stent making to find the better laser cutter. From the analysis, it is observed that the Fiber laser is one of best alternatives among other five different lasers. By comparing with other five different lasers, the fiber laser stent maker has better beam quality, low cost, low maintenance, high stability and reliability, low power consume and high cutting speed. By utilizing this fiber laser for stent making, the manufacturing company gets profit and also can provide the stent for coronary artery patients with low cost and high beam quality. Further, this work can be studied by using other MADM techniques such as VIKOR, fuzzy AHP and other hybrid techniques.

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References 1. Abbasbandy, S., Hajjari, T.: A new approach for ranking of trapezoidal fuzzy numbers. Comput. Math. Appl. 57(3), 413–419 (2009) 2. Bera, B., Shit, P.K., Sengupta, N., Saha, S., Bhattacharjee, S.: Susceptibility of deforestation hotspots in Terai-Dooars belt of Himalayan Foothills: a comparative analysis of VIKOR and TOPSIS models. J. King Saud. Univ. Comput. Inf. Sci. (2021). https://doi.org/10.1016/j.jks uci.2021.10.005 3. Deva, N., Felix, A.: Designing DEMATEL method under bipolar fuzzy environment. J. Intell. Fuzzy Syst. 41(6), 7257–7273 (2021) 4. Felix, A., Dash, S.K.: Haar FCM with DEMATEL techniques to analyze the solid waste management. Adv. Smart Grid Technol. Lect. Notes Electr. Eng. 688, 393–402 (2021). https:// doi.org/10.1007/978-981-15-7241-8_28 5. Guerra, A., Ciurana, Q.D.: Stent manufacturing field: past, present, and future prospects. Angiography 73817, 41–60 (2019) 6. Guerra, A., De Ciurana, J.: Fibre laser cutting of polymer tubes for stents manufacturing. Procedia Manuf. 13, 190–196 (2017) 7. Hwang, C.L., Yoon, K.S.: Multiple Attribute Decision Making: methods and Applications. Springer, Berlin, Germany (1981) 8. Kumar, S., Kumar, S., Barman, A.G.: Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit. Procedia Comput. Sci. 133, 905–912 (2018) 9. Ligus, M., Peternek, P.: Determination of most suitable low-emission energy technologies development in Poland using integrated fuzzy AHP-TOPSIS method. Energy Procedia 153, 101–106 (2018) 10. Lo, H.W., Liou, J.J.H.: Comments on “Factors influencing medical tourism adoption in Malaysia: a DEMATEL-Fuzzy TOPSIS approach.” Comput. Ind. Eng. 157, 107333 (2021). https://doi.org/10.1016/j.cie.2021.107333 11. Marchand, É., Strawderman, W.E.: Pro of Jou rna lP. Stat. Probab. Lett. 108799, (2020). https:// doi.org/10.1016/j.dajour.2021.100021 12. Meier, B.: Stenting, quite a legacy of Charles, Charles, and Arthur. Cathet. Cardiovasc. Diagn. 45(3), 233–234 (1998) 13. Selvaraj, A., Dash, S.K., Punithavelan, N., Felix, A.: Fuzzy TOPSIS approach to identify the flood vulnerability region in South Chennai. Int. J. Pure Appl. Math. 118(3), 667–674 (2018) 14. Steen, W.M.: Laser Material Processing, 4th edn. Springer, London (2010) 15. Tamai, H., Igaki, K., Kyo, E., Kosuga, K., Kawashima, A., Matsui, S., Komori, H., Tsuji, T., Motohara, S., Uehata, H.: Circulation 102, 399–404 (2000) 16. Chodha, V., Dubey, R., Kumar, R., Singh, S., Kaur, S.: Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques. Mater. Today: Proc. (2021) 17. Venkatraman, S.S., Tan, L.P., Joso, J.F.D., Boey, Y.C.F., Wang, X.: Biomaterial 27, 1573–1578 (2006) 18. Whittaker, D.R., Fillinger, M.F.: The engineering of endovascular stent technology: a review. Vasc. Endovasc. Surg. 40(2), 85–94 (2006) 19. WHO. World Health Organization; Geneva: 2016. Global Health Estimates 2015: Deaths by cause, Age, Sex, by Country and by Region, 2000–2015 20. Zadeh, L.A., Klir, G.J., Yuan, B.: Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers, p. 6 (1996) 21. Zadeh, L.A.: Fuzzy sets. In: Zadeh, L.A. (ed) Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: selected Papers, pp. 394–432 (1996)