Recent Trends in Mechanical Engineering: Select Proceedings of ICOFTIME 2020 (Lecture Notes in Mechanical Engineering) 9811620857, 9789811620850

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Lecture Notes in Mechanical Engineering

C. S. Ramesh Praduymna Ghosh Elango Natarajan   Editors

Recent Trends in Mechanical Engineering Select Proceedings of ICOFTIME 2020

Lecture Notes in Mechanical Engineering Series Editors Francisco Cavas-Martínez, Departamento de Estructuras, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesco Gherardini, Dipartimento di Ingegneria, Università di Modena e Reggio Emilia, Modena, Italy Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia Vitalii Ivanov, Department of Manufacturing Engineering Machine and Tools, Sumy State University, Sumy, Ukraine Young W. Kwon, Department of Manufacturing Engineering and Aerospace Engineering, Graduate School of Engineering and Applied Science, Monterey, CA, USA Justyna Trojanowska, Poznan University of Technology, Poznan, Poland Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany

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C. S. Ramesh · Praduymna Ghosh · Elango Natarajan Editors

Recent Trends in Mechanical Engineering Select Proceedings of ICOFTIME 2020

Editors C. S. Ramesh Mechanical Engineering Presidency University Bengaluru, India

Praduymna Ghosh Mechanical Engineering Indian Institute of Technology BHU Varanasi, India

Elango Natarajan Mechanical Engineering UCSI University Kuala Lumpur, Malaysia

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-16-2085-0 ISBN 978-981-16-2086-7 (eBook) https://doi.org/10.1007/978-981-16-2086-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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

Contents

Experimental Investigation of Performance, Emission and Combustion Characteristics of Diesel Engine Using Waste Cooking Oil-Derived Biodiesel with Ethanol with Application of EGR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashley Lobo and D. K. Ramesha

1

Structural Modelling of Hydrocarbons for the Prediction of Octane Number and Designing of Sustainable Synthetic Fuel . . . . . . . . . . . . . . . . . Sanjay Kumar, Mamta Thakur, Naman Shah, and Sarthak Jain

19

Optimization of Wear Behavior of Aluminum–Boron Carbide Composites Using Factorial Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Sanman, K. P. Prashanth, and G. N. Lokesh

37

Effect of Carbon Nanoparticles on the Mechanical Properties of Banana Fiber Reinforced Polymer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Navid Bin Mojahid, Kamrujjaman Rubel, Sayed Samiul Newaz, and Nikhil R. Dhar

49

A Study on Tensile and Tear Properties for Chitosan Blended with and Without Natural Fiber Films . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. P. Prashanth, S. Sanman, and G. N. Lokesh

61

Effect of Cryogenic Treatment on Mechanical Properties of Al– SiC Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Raghavendra, N. Satish, and B. S. Ajay Kumar

75

Influence of Various % of Carbon Nanotubes Reinforced AZ91 Magnesium Alloy Nano Composite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. M. Sandeep, Bopanna Satish Babu, N. Vinayaka, Muralidhar, and S. L. Vijay Kumar Re-design and Analysis of Brick Trolley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. M. Sanjay Kumar, S. Vinay Kumar, R. Manoj Bevoor, L. J. Chirayu, and A. Uday

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Contents

Design and Analysis of Kevlar Fiber Reinforced Composite Leaf Spring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 M. V. Puneeth kumar, Satish Kulakarni, M. H. Venkatesh, Sateesh Nayaka, and P. Sharath Analysis of Crack Growth Behavior of Fan Rotor Blade Under Centrifugal Force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 K. S. Syed Azam Pasha, Basavaraj, and T. V. Manjunath Comparative Hydrodynamic Investigations on Unmanned Aquatic Vehicle for Ocean Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 R. Sankaresh Pandian, R. Vijayanandh, S. Kishor Kumar, V. Praveen Kumar, M. Ramesh, M. Senthil Kumar, and G. Raj Kumar Factors Influencing the Non-implementation of TPM in the Selected Manufacturing Industries: A Statistical Approach . . . . . . . . . . . . . . . . . . . . 153 M. Prashanth Pai, C. G. Ramachandra, T. R. Srinivas, and M. J. Raghavendra Optimization of EDM Machining of High Carbon High Chromium Steel Using Zirconium and Nickel Powder Mixed Dielectric by Grey Relational Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 S. Ramesh, N. Vijayakumar, R. Viswanathan, and S. Saravanan Lean Implementation and Assessment Within Four Indian SME’s-A Case Studies Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 J. P. Rishi, C. G. Ramachandra, T. R. Srinivas, and B. C. Ashok A Study on Effect of Chill Casting on Dry Sand Abrasive Wear Behaviour on A356 Reinforced with Hematite Metal Matrix Composite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 M. Sunil Kumar, N. Sathisha, and Batluri Tilak Chandra Fracture Studies of Austempered Ductile Iron . . . . . . . . . . . . . . . . . . . . . . . . 205 J. V. Raghavendra and K. Narasimha Murthy

About the Editors

Dr. C. S. Ramesh obtained his Ph.D. in Metallurgical Engineering with academic distinction from Indian Institute of Technology, Madras, Chennai, India, in 1992 in the area of composite coatings with Bachelors in Mechanical Engineering from U.V.C.E, Bangalore and Masters in Metal Casting from Bangalore University. He has over 22 years of experience in teaching and research. His current research interests are thermal spray coatings, metal matrix composites, finite element methods related to bio-implants. Being passionate about research, he has executed projects of national and international importance funded by several government and private agencies such as Naval Research Board, Aeronautical Research Board, Combat Vehicles Research & Development Establishment, Department of Science & Technology, Indian Space Research Organisation, GE- India, Allegion, BHEL, and AICTE. A state-of-the-art facilities in rapid prototyping, metal forming, tribology, thermal spray & CAE has been set up by him at PESIT, Bangalore. He has contributed over 150 papers in international, national journals & conferences, 05 book chapters and authored 06 technical reports. He has to his credit guided 14 Ph.Ds and over 50 postgraduate students. Recognizing the research activities, Government of Karnataka has conferred Dr. Ramesh with Professor Satish Dhawan Award for Young Engineers for Outstanding Contribution to Engineering Science in 2008. He received the Best Research Publication Award by Vision Group of Science & Technology, Karnataka, 2010 and Sudharshan Bhat Award from IIT Madras, 1992, for Best Ph.D. thesis. Under Fusion Fund International Exchange Programme Prof. Ramesh was a Visiting Professor at School of Design, Engineering & Computing, Bournemouth University, UK (2012). Dr. Praduymna Ghosh is Professor at Department of Mechanical Engineering, IITBHU, Varanasi, India. He did his Ph.D. from Department of Mechanical Engineering, Institute of Technology (Banaras Hindu University), Varanasi, India; M.Tech from IIT Bombay; and B.E (Mechanical Engineering) from Bengal Engineering College, Shibpur, West Bengal. Before joining academics he had five years of experience in research and industry at TERI, New Delhi; University of Maryland, College Park, USA; and Infosys Technologies Ltd. His area of research is microgravity fluid physics, flow through porous media, nanofluids. He has authored 40 international vii

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About the Editors

journal publications and one book, “Thermal Convection in Microgravity” and one book chapter in “Heat Transfer in Humans”, John Willey, UK. Prof. Ghosh is a reviewer of many international journals like ASME heat transfer, IJHMT, Transport in Porous Media, Energy, International Journal of Thermal Science. He is also an expert committee member of DST, Government of India. Dr. Elango Natarajan is a Chartered Engineer (CEng.) awarded by Engineering Council, UK. He was awarded Ph.D. in Mechanical Engineering from Anna University, Chennai, India, in 2010. He was associated with Center for Artificial Intelligence and Robotics (CAIRO) in UTM, Skudai, Malaysia in 2013 as a post doctoral research fellow. He has been serving for engineering colleges/universities for about 20 years in various academic positions. He has published 48 articles in ISI/Scopus publications till today. He has completed two external grants under Ministry of Higher Education (MOHE), Malaysia, and two internal grant projects, supported by UCSI University. He is now working with two external grants received from MOHE, Malaysia. He has been involved in curriculum development, accreditation, professional membership. He is associated with professional bodies like IEEE, IET and BEM. He is an executive committee member of IEEE/RAS, Malaysia.

Experimental Investigation of Performance, Emission and Combustion Characteristics of Diesel Engine Using Waste Cooking Oil-Derived Biodiesel with Ethanol with Application of EGR Ashley Lobo

and D. K. Ramesha

Abstract While electric and hybrid vehicles do act as credible replacement option for diesel engines, reality is that they will not replace it anytime soon. This leaves consumers with no choice but to prefer a diesel car over the other options. In addition, diesel engines are notorious for the emissions they emit which cause damage to health and the environment. This calls for urgency of action on emissions of diesel engine till the other viable options become better. Another major problem is the shortage of conventional fuels, especially diesel. In the present work, biodiesel derived from waste cooking oil has been employed as fuel along with ethanol (which serves as an oxygenated additive) and is considered for investigation. A Kirloskar made TV2 diesel engine has been employed to conduct the experiment at varied load. In this experiment, diesel has been considered as baseline reading, and two other fuels, that is biodiesel and biodiesel+ ethanol, have been considered for comparison purpose. The engine has been run from 0 to 100% load using the three fuels and the results have been evaluated, tabulated and plotted on graphs for discussion. From the results, it is revealed that the brake thermal efficiency (BTE) is increased by nearly 10% and brake-specific fuel consumption (BSFC) has reduced by 12%, thus showing that the performance of the engine is improved. The emissions of carbon monoxide (CO), nitrogen oxide (NOx) and particulate matter (PM) have significantly reduced; especially NOx has reduced by nearly 30%. There is also improvement of combustion with the heat release rate (HRR) increasing by nearly 15% and introduction of ignition delay. Exhaust gas recirculation (EGR) has also been used. EGR provides the best results when used as an after exhaust treatment system and is detrimental to NOx

A. Lobo (B) Department of Mechanical Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India e-mail: [email protected] D. K. Ramesha Department of Mechanical Engineering, University Visvesvaraya College of Engineering, Bengaluru, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 C. S. Ramesh et al. (eds.), Recent Trends in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-2086-7_1

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A. Lobo and D. K. Ramesha

formation which is the most notorious emissions from a diesel engine and the results gotten from it were promising. Thus by employing this additive, the emissions are decreased without penalizing the performance and combustion characteristics. Keywords Waste cooking oil methyl ester · Exhaust gas recirculation · Performance · Emission · Combustion · Diesel engine

1 Introduction As crude oil prices rise, the necessity for developing alternate fuels that are economically viable is acute [1]. Using of fossil fuels has increased because of its use in power plants, vehicles, generators, mining machinery and railway engines [2]. Increase of fossil fuels also means increase in the amount of emissions emitted while using these fuels, and with global warming increasing this is a very serious issue. Energy consumption of the world almost increased by twice from 1971 compared to 2001 and is forecasted to increase by more than 50% by 2030. Reports [3] state that depletion of fuel reserves is eminent and will happen in another 41–63 years if the current consumption rate continues; moreover, variability of crude oil prices is a serious threat for smaller countries with limited funds [4]. The alternative forms of energy which are gotten from renewable sources are still in its nascent stages of development and lacks self-assurance for large-scale implementation. Biodiesel is vital in overcoming fossil fuel shortage and the environmental damage caused by the use of fossil fuels [5]. There still isn’t a robust way to collect waste cooking oils castoff from domestic usage and disposing it through the drainage system is harmful to the environment as it results in water pollution. More than 75% of waste cooking oils (WCO) comes from domestic usage and controlling its dumping requires a lot of investment [6, 7]. Using this discarded WCO for production of biodiesel is one of the best ways to properly dispose it and would also decrease the reliance on diesel [8, 9]. Between 2010 and 2015, the utilization of WCO has increased steadily, resulting in a 370% rise in its use, increasing from 690,000 tonnes to 2.47 million tonnes [10]. Among various types of biofuels, WCO poses as an important alternate fuel to diesel fuel; hence the present paper aims to study the utilization and behavior of WCO as fuel in a diesel engine operation. With the application of biodiesel, there are disadvantages such as high density and lower calorific value. Fuel additives play a crucial role in curtailing the drawbacks of biodiesel and to make sure it can meet the set international fuel standards. Additives are applied to improve the combustion, emission and performance characteristics. Oxygenator is one such additive which is used because it rises the oxygen amount of the fuel that facilitates for better and more complete combustion [11, 12]. When ethanol is added, it increases the oxygen amount of the blend which leads to better overall combustion, thereby reducing the emissions as emissions are mainly caused due to lack of oxygen availability while combustion. Also, since a part of the conventional fuel is replaced by ethanol, it also helps cut

Experimental Investigation of Performance …

3

down on the burning of diesel fuel [13]. Ethanol is a brilliant oxygenator and when blended with biodiesel fuel has the added advantage of reducing emissions of CO and smoke opacity from engines employing the blends [14]. India’s Biofuel Policy 2018 has insisted on an Ethanol Blending Program (EBP) where the blending target of ethanol was set to 10% by 2022 and 20% by 2030, while biodiesel blending target has been set at 5% by 2030 which will help India’s efforts to cut energy imports and carbon emissions. India is the world’s third largest oil importer, and witnessed a 25% year-on-year increase in its oil import bill in 2018 to $112 billion. In March 2015, the government had set an objective of dropping import dependency of oil by 10 percentage points to 67% by 2022. The policy is also aimed at improving farmers’ income and has expanded the scope of raw material for ethanol production to include sweet sorghum sugarcane juice, starch containing material such as corn, cassava, damaged grain and sugar beet. The sugar industry has reacted positively by hugely investing in new or expansion of ethanol production capacities, which will help achieve the government’s 10% ethanol blend targets by 2022 (EBP). Financial incentive coupled with a surplus sugar season were the reasons for conversion of excess sugar to ethanol. India’s total ethanol consumption in 2020 is predicted to rise 22% to a record 3.8 billion liters, as compared to 3.1 billion liters consumed a year ago. Also, ethanol production is expected to reach 3 billion liters in 2019, 11% higher as compared to 2.7 billion liters produced in 2018. India’s biofuel policy presents an opportunity for research in the field of biodiesel and ethanol. This paper intends to study the effects of a diesel engine on performance, emissions and combustion characteristics when both ethanol and waste cooking oil biodiesel are blended. Formation of NOx is a temperature phenomenon and to reduce it requires reduction of temperature in the cylinder. Exhaust gas recirculation (EGR) has been widely acknowledged to be the most feasible method to reduce NOx emissions with great effect. In EGR a small portion of the exhaust gas is recycled to the engine which reduces the oxygen available for the harmful emissions, and thus leading to reduction of the harmful emissions. Hot EGR is a widely used technique in EGR which basically keeps the exhaust temperature inflated and diminishes NOx, smoke opacity and HC. In the EGR process the recirculated air increases the specific heat capacity of the mixture and decreases the oxygen concentration of the intake mixture, leading to significant reduction in harmful emissions. Another point to note is that EGR doesn’t not affect the fuel efficiency [15, 16]. In the present work, waste cooking oil (WCO) has been used as biodiesel along with oxygenator additive—ethanol, and the performance characteristics, emission characteristics and combustion characteristics of the diesel engine with varied load are studied. In order to ensure that the WCO blend can be used in a diesel (compression ignition) engine, transesterification has been carried out to get waste cooking oil methyl ester. Hot exhaust gas recirculation has been employed for further reduction of emission.

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Table 1 Properties of fuels Properties

Diesel

WCO (Waste cooking oil)

WCOME (WCO methyl ester)

B20WCOME (20%WCOME + 80%Diesel)

Ethanol

B20WCOME + 15%Ethanol

Kinematic viscosity (40 °C)

3.05

43

5.83

4.01

1.2

3.765

Heat value (MK/kg)

44.5

29

38

42.9

27.9

36.8

Density (kg/m3 )

830

914

887

842

790

828.75

Flash point °C 60

307

150

98

14

79

Cetane number

40

53

51.48

49.18

110 (Octane number)

48

Iodine number

6

125

59

17.42

–

57

2 Preparation of Fuel 2.1 Transesterification There were totally three blends used: pure diesel, B20 (20% waste cooking oil methyl ester {WCOME} +80% diesel) and B20 WCOME+ 15% ethanol. The biodiesel was obtained from the transesterification process. Transesterification has been used to convert WCO, which is predominantly triglycerides, to glycerol, using methanol as the alcohol and potassium hydroxide as a catalyst. The steps carried out in transesterification is similar to Lobo [17]. The properties of the fuels used in experimentation are given in Table 1.

2.2 Experimental Setup For experimentation, ethanol is blended with WCOME and this biofuel is used in a Kirloskar TV2 (shown in Fig. 1) engine and the technical specification of the engine is mentioned in Table 2. The TV2 engine has been loaded from 0 to 100% for investigation and all the values were plotted on a graph for easy comparison. In total, three blends are used for experimentation which are diesel, B20 WCOME(20% WCOME + 80% diesel) and B20 WCOME + 15% ethanol.

Experimental Investigation of Performance …

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Fig. 1 Kirloskar TV2 engine used for this experimentation

Table 2 Technical specifications of the engine

Manufacturer

Kirloskar oil engines Ltd

Model

TV2

No. of cylinder

Two

Type of engine

Vertical, 4-stroke cycle, single acting

Cooling

Water

Fuel

Diesel

HP

16 HP

Starting

Hand cranking

Bore

87.5 mm

Stroke

110 mm

Cubic capacity

1322 cc

Compression ratio

17.5:1

Valve clearance inlet

0.18 mm

Valve clearance exhaust 0.20 mm

2.3 Error Analysis and Uncertainty The experimental results will certainly have error and uncertainties, which can rise from the incorrect calibration of instruments due to excessive handling and mishandling, surrounding conditions, experimental test conditions and planning, surveillance and reading. The errors, which are gotten, become a hindrance to obtain accurate results. Thus to nullify these errors, tools and methods from mathematics and statistics are used. Generally, the method used is repetition of the taking data from

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Table 3 Error analysis and uncertainty table

Measurements

Accuracy

Uncertainty (%)

Speed

±3 RPM

± 0.3

BSFC

±3 kg/KWh

± 0.35

Power(KW)

±0.3 KW

± 0.40

CO

±0.02%

± 1.0

NOx

±7 ppm

± 0.7

In cylinder pressure

±0.1 bar

± 0.2

Temperature

±1 °C

± 0.1

HC

±7 ppm

± 0.7

Torque

± 0.1 Nm

±1

the experimentation (at least three times) and finding the mean in order to minimize the error which may occur [18]. Measurements of uncertainties were calculated and results are shown in Table 3. To calculate the percentage of uncertainty occurring in experiments, the percentage uncertainties in measuring various parameters were determined using the root-sum-square method as shown in Eq. 1. It is calculated by taking the square root of the sum of the squares of the percentage uncertainties of various physical quantities (given in Table 3) such as speed, brake thermal efficiency, power, specific fuel consumption, etc. Percentage of Uncertainty occurring in Experiments = √ ((Speed)2 + (Break Specific Fuel Consumption)2 + (Power)2 + (CO)2 + (NOx)2 + (Pressure)2 + (Temperature)2 + (HC)2 + (Torque)2 )

(1)

The calculated uncertainty of the experiment was 1.8445%.

3 Results and Discussions Experimentation has been conducted and graphs have been tabulated and plotted. The different characteristics which was measured are: Performance: (P-θ) and heat release rate (HRR), emissions: carbon monoxide (CO), smoke opacity, nitrogen oxides (NOx), hydrocarbons (HC), and combustion: brake thermal efficiency (BTE) and brake-specific fuel consumption (BSFC).

Experimental Investigation of Performance …

7

3.1 Brake Thermal Efficiency (BTE) Figure 2 shows the variation of brake thermal efficiency with load. In Fig. 2 it is observed that the BTE for B20 and B20 + ethanol is greater for all loads, and at 100% load it is increased by 2 and 7% when compared to diesel. This can be accredited to the improved diffused combustion which would have also resulted due to oxygen enhancement caused by the blends and this is supported by the fact that HRR process almost occurs at the same location for the biodiesel + ethanol blend [19]. When EGR is applied to the B20 + ethanol blend there is a decrease of nearly 3% compared to diesel, because for EGR to work there has to be a pressure difference between the inlet and exhaust manifolds to make sure the exhaust gases reach the inlet manifold and partially replace the air used for combustion. This is achieved by throttling the air of the inlet flow which adds extra load on the engine because it rises the pumping work. Thus for the same output more work has to be done, thereby decreasing the thermal efficiency. This is offset by pumping more fuel but increases the BSFC when EGR is applied [20].

Fig. 2 Variation of brake thermal efficiency with load

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Fig. 3 Variation of brake-specific fuel consumption with load

3.2 Brake-Specific Fuel Consumption (BSFC) Figure 3 shows the variation of brake-specific fuel consumption with load. From Fig. 3, it is very clear that BSFC reduces as load increases at 100% load and it is 10% for B20 and 14% for B20 + Ethanol. There are two main aspects to be considered here, self-ignition temperature and boiling point. The ethanol self-ignition temperature is greater than that of diesel but for boiling point it is vice versa, which means diesel will initiate the ignition but due to the boiling point of ethanol being lower, it will evaporate before diesel and will sustain the progress of the combustion through the unburned blend spray, thereby reducing consumption of excess fuel [21]. Another reason is the additives decrease the mass flow rate because the density decreases as there is an increase in the % of additives. When EGR is used for the ethanol blend the BSFC reduces by 7%. The slight rise of BSFC when compared to without EGR for the blend is because of altering the A/F (air to fuel) ratio, which creates an oxygen deficit, dilution effect and the dwindling burn rate, making stable state of combustion harder to attain [22, 23].

3.3 Heat Release Rate (HRR) Figure 4 illustrates the variation of HRR with load, and on closer inspection it shows that there is a decrease of nearly 13% compared to diesel and 7% when compared to biodiesel when ethanol is added to biodiesel for 100% load and a decrease of

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Fig. 4 Variation of heat release rate with load

22% and 7% when paralleled to diesel and biodiesel at 80% load. The reason is the lower heating value of the blends as compared to the conventional fuel and lower combustion temperature at the end of the cycle [24]. A possible reason for this reduction could be the cooling effect instigated by vaporization of the fuel and loss of heat from the walls of the engine cylinder [25]. Another point to note is that with biodiesel the calorific value is lesser than diesel which will result in lower HRR. When EGR is applied there is negligible change of the heat release rate (Fig. 5).

3.4 Cylinder Pressure V/S Crank Angle P vs Theta gives us the maximum pressure which occurs in the engine and exactly when it occurs after top dead center (TDC). The reason this graph is important is because it gives us the actual work output/input of a machine. Ideally the peak pressure should be before 15°–20° from TDC, then the work is converted to useful work, else it gets expelled out as very hot gas which again exacerbates the emissions. Thus the position where maximum peak pressure occurs is of utmost importance. From Fig. 5 at 100% load, the position of when peak pressure takes place is almost the same but the decrease in peak pressure value for B20 and B20 + Ethanol is 7% and 10%, respectively. The peak pressure in the cylinder is directly correlated to the quantity of accumulated fuel present within the first phase and as pressure increases,

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Fig. 5 Variation of cylinder pressure with crank angle

the delay reduces because of better atomization of the fuel, which in turn leads to lesser buildup of fuel in first phase causing decrease of peak pressure [26]. When EGR is applied the peak pressure moves to the right of TDC by 6° and the maximum peak pressure of the ethanol blend is reduced by 12% when compared to diesel and 18% when compared to blend without EGR. Decrease in peak pressure is when EGR is applied due to the different composition which EGR introduces, mainly carbon dioxide. Moreover, this subsequently increases as load increases [27].

3.5 Smoke Opacity Figure 6 shows the variation of smoke opacity with load. It can be seen from Fig. 6 that the smoke opacity reduces by 9% for B20 and 7% for B20 + ethanol compared to diesel. The reduction in oxygen causes decline of the flame temperature, which again is negative for soot formation [28]. Another reason for reduction of smoke opacity is bonded oxygen tends to reduce the soot formation, and with ethanol being added to biodiesel the amount of bonded oxygen increases which results in lower smoke opacity [29]. Not to forget decline of aromatic compounds (which are considered soot precursors) because ethanol does not provide the preliminary radicals which are required for the development of aromatic rings in the biodiesel + ethanol fuel, and also the configuration of soot from biodiesel is different from that of diesel which may favor oxidation leading to lower smoke opacity [30–33]. When EGR is

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Fig. 6 Variation of smoke opacity with load

applied for the blend it is seen that smoke opacity decreases by 11% for biodiesel + ethanol blend compared to diesel because of drop in the available oxygen level in the combustion chamber which is unfavorable for development of soot. The same results was seen by Zheng et al. [34] and Qi et al. [23].

3.6 Hydrocarbon Emissions (HC) From Fig. 7 it is evident that HC emissions decrease for the blends when compared to the conventional fuel, and at 100% load the unburnt hydrocarbon emissions of B20 reduced by 22% and B20 + Ethanol reduced by 19% compared to diesel. Lower excess oxygen content results in improper combustion. However, this deficit in oxygen is countermanded when B20 is added because B20 has molecular oxygen, which decreases the oxygen necessary for combustion resulting in lower HC emissions [35]. Biodiesel has greater oxygen content, which can accomplish improved combustion and lessen the HC emissions [36]. The occurrence of oxygen in the blends increases the post flame oxidation process of unburnt hydrocarbons (UBHCs) in the combustion chamber [37]. The same results was seen by Pradeep and Sharma [38].

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A. Lobo and D. K. Ramesha

Fig. 7 Variation of hydrocarbon emissions with load

3.7 Carbon Monoxide Emissions (CO) Figure 8 illustrates the variation of carbon monoxide with load. At 80% load there is a decrease of 36% and 28% of B20 and B20 + Ethanol and at full load it is 30% and 27%, respectively. Cetane number reduction is a major drawback of ethanol though this is mostly counterbalanced by addition of biodiesel, and there still is a reduction of it as shown in the fuel properties. Added to that, the drop in cetane number is also partially due to incomplete combustion of the ethanol–air mixture. This reduction of cetane number leads to lower CO [39]. Enrichment of oxygen is another major factor. When the biodiesel is added as aforementioned it is rich in oxygen, and this increase in oxygen content leads to carbon molecules being burned and combusted, and thus releases CO2 rather than the poisonous CO [40]. When EGR is applied, the emissions are slightly increased by about 4% compared to without EGR for the same blend, and overall the CO emission is decreased by about 21% for the B20 + ethanol blend with EGR compared to diesel. Carbon monoxide tends to increase because of the lower oxygen which is available for combustion when EGR is applied, which results in rich air–fuel mixtures at different regions in the combustion chamber.

Experimental Investigation of Performance …

13

Fig. 8 Variation of carbon monoxide with load

3.8 Nitrogen Oxides (NOx) From Fig. 9 it is clearly visible that at full load the NOx emissions reduce by 15 and 12% for B20 and B20 + ethanol, respectively, when compared to diesel. Due to the poorer iodine number of WCO, which is around 59, it warranted the presence of the extra saturated fatty acids in B20 [41–46]. The cetane number of biodiesel is superior compared to diesel due to the longer chains fatty acids and higher degrees of saturation [lower iodine number] which leads to lower NOx emission [47–49]. EGR includes replacing the air used for combustion by CO2 and H2 O vapor which has higher specific heat capacity than the main components of air, which is oxygen and nitrogen, leading to lower gas temperatures. Decrease in oxygen content, due to it being replaced in EGR, also leads to lower flame temperature and thereby reducing NOx emissions of the ethanol blended biodiesel by 22% when compared to diesel when NOx is applied to the ethanol blend as formation of NOx is a highly temperature-dependent phenomenon [50].

4 Conclusions • Waste cooking oil is abundantly available throughout the world and its disposal poses a great problem, but its potential as an alternate fuel is very promising. Transesterification has been employed to convert WCO to biodiesel and also the

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A. Lobo and D. K. Ramesha

Fig. 9 Variation of nitrogen oxides with load

•

•

•

•

properties have been evaluated according to the ASTM standard, and the biodiesel is compatible with diesel. Brake thermal efficiency is increased by nearly 7% for the biodiesel ethanol blend when comparted to diesel at full load due to enhancement of oxygen caused by addition of an oxygenator like ethanol. When EGR was applied to this mixture there is a marginal decrement of 3% caused by the pressure difference in inlet manifold required to apply EGR. Brake-specific fuel consumption is increased by 10% due to higher self- and lower boiling point ignition of ethanol additive compared to diesel which causes it to evaporate before diesel while combustion, and thus support more complete combustion. Heat release rate of the ethanol blended biodiesel is increased by 13% because of lower heating value of the biodiesel blends. P–θ diagram shows that there is marginal decrement in the value of the maximum value of peak pressure at full load caused mainly by ignition delay. EGR decreases the value of peak pressure by 18% mainly due to the introduction of CO2 caused by the introduction of EGR. Smoke opacity of the exhaust gases decreases by 7% at 100% load because of reduction of aromatic compounds in the blends which are the soot precursors. When EGR is applied to the blend, the smoke opacity reduces by 11% at full load which is bought about by drop in the level of available oxygen for the development of soot when EGR is applied.

Experimental Investigation of Performance …

15

• For hydrocarbon and carbon monoxide emissions it is reduced by nearly 25% caused by enrichment of oxygen content present in the fuel by addition of an oxygenator. • Nitrogen oxides/NOx reduce by 12% for the ethanol blended fuel mainly because of the lower iodine number of the biodiesel blend. For EGR the reduction in NOx is mainly because of lack of oxygen present in the combustion chamber for formation of NOx. Hence this fuel can be applied as an alternate fuel along with ethanol as the additive and EGR can be used for reducing emissions, without compromise on performance or combustion characteristics.

References 1. M. Chhetri, A.B. Watts, K.C. Islam, M. Rafiqul, Waste cooking oil as an alternate feedstock for biodiesel production. https://doi.org/10.3390/en1010003 2. S. Ahmed, M.H. Hassan, M.A. Kalam, S.A. Rahman, M.J. Abedin, A. Shahir, An experimental investigation of biodiesel production, characterization, engine performance, emission and noise of Brassica juncea methyl ester and its blends. J. Cleaner Prod. 79, 74–81 (2014) 3. British Petroleum Statistical Review of World Energy June 2007 [Online]. www.bp.com/statis ticalreview. Accessed Feb 2020 4. G. Santori, G.D. Nicola, M. Moglie, F. Polonara, A review analyzing the industrial biodiesel production practice starting from vegetable oil refining. Appl. Energy 92, 109–132 (2012) 5. E.G. Giakoumis, A statistical investigation of biodiesel effects on regulated exhaust emissions during transient cycles. Appl. Energy 98, 273–291 (2012) 6. M.K. Lam, K.T. Lee, Renewable and sustainable bioenergies production from palm oil mill effluent [POME]: win-win strategies toward better environmental protection. Biotechnol. Adv. 29, 124–141 (2011) 7. M. Atapour, H.R. Kariminia, Characterization and transesterification of Iranian bitter almond oil for biodiesel production. Appl. Energy 88(7), 2377–2381 (2011) 8. M.G. Kulkarni, A.K. Dalai, Waste cooking oils: an economical source for biodiesel: a review. Ind. Eng. Chem. Res. 45, 2901–2913 (2006) 9. D. Phillips, Implications of imported used cooking oil [UCO] as a biodiesel feedstock (NNFC York, UK) 10. C. Calderón, G. Gauthier, J.M. Jossart, Bioenergy Europe Pellet Report 2018. European Pellet Council [Online]. https://epc.bioenergyeurope.org/bioenergy-europe-pellet-report2018/. Accessed Feb 2019 11. S.K. Hoekman, A. Broch, C. Robbins, E. Ceniceros, M. Natarajan, Review of biodiesel composition, properties, and specifications. Renew. Sustain. Energy Rev. 16, 143–169 (2012) 12. S. Sivalakshmi, T. Balusamy, Effect of biodiesel and its blends with diethyl ether on the combustion, performance and emissions from a diesel engine. Fuel 106, 106–110 (2013) 13. A.R. Patil, S.G. Taji, Effect of oxygenated fuel diethyl malonate additive on diesel engine performance and emission. J. Mech. Civil Eng. 7, 58–62 14. N. Marek, J. Evanoff, Pre-commercialization of E-diesel fuels in off-road applications, in Proceedings of A&WMA 2002; Annual Conference, paper No. 42740 15. M. Duernholz, H. Endres, Exhaust-gas recirculation. A measure to reduce exhaust emissions of DI diesel engines, International Congress and Exposition (February 24, 1992)) 16. G.H. Abd-Alla, Using exhaust gas recirculation in internal combustion engines: a review. Energy Convers. Manag. 43, 1027–1042 (2002)

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17. A. Lobo, Influence of zinc oxide on direct injection ci engine fueled with waste cooking oil biodiesel (2019). https://doi.org/10.17148/iarjset.2019.61106 18. A. Kakoee, Y. Bakhshan, S.M. Aval, A. Gharehghani, An improvement of a lean burning condition of natural gas/diesel RCCI engine with a pre-chamber by using hydrogen. Energ. Convers. Manag. 166, 489–499 (2018) 19. D.B. Hulwan, S.V. Joshi, Performance, emission and combustion characteristic of a multicylinder DI diesel engine running on diesel–ethanol–biodiesel blends of high ethanol content. Appl. Energy 88, 5042–5055 (2011) 20. N. Ladommatos, R. Balian, R. Horrocks, L. Cooper, The effect of exhaust gas recirculation on soot formation in a high-speed direct-injection diesel engine, SAE Technical Paper No. 960841 21. A. Bilgin, O. Durgun, Z. Sahin, The effects of diesel-ethanol blends on diesel engine performance. Energy Sources 24, 431–440 (2002) 22. H. Saleh, Effect of exhaust gas recirculation on diesel engine nitrogen oxide reduction operating with jojoba methyl ester. Renew Energy 34, 2178–2186 (2009) 23. D. Qi, M. Leick, Y. Liu, C.-F. Lee, Effect of EGR and injection timing on combustion and emission characteristics of split injection strategy DI-diesel engine fueled with biodiesel. Fuel 90, 1884–1891 (2011) 24. K. Cheenkachorn, B. Fungtammasan, Biodiesel as an additive for diesohol. Int. J. Green Energy 6, 57–72 (2009) 25. K. Murlidharan, D. Vasudevan, Performance, emission and combustion characteristics of a variable compression ratio engine using methyl esters of waste cooking oil and diesel blends. Appl. Energy 88, 3959–3968 (2011). https://doi.org/10.1016/j.apenergy.2011.04.014 26. S. Puhan, R. Jegan, K. Balasubbramanian, G. Nagarajan, Effect of injection pressure on performance, emission and combustion characteristics of high lino- lenic linseed oil methyl ester in a DI diesel engine. Renew. Energy 34, 1227e33 (2009) 27. N. Ladommatos, S. Abdelhalim, H. Zhao, Z. Hu, The effects of carbon dioxide in exhaust gas recirculation on diesel engine emissions. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 212, 25–42 (1998) 28. R.P. Wilson, E.B. Muir, F.A. Pellicciotti, Emissions study of a single-cylinder diesel engine, SAE Papers 740123 (1974) 29. M. Lapuerta, J.M. Herreros, L.L. Lyons, R. García-Contreras, Y. Briceño, Effect of the alcohol type used in the production of waste cooking oil biodiesel on diesel performance and emissions. Fuel (2008). https://doi.org/10.1016/j.fuel.2008.05.013 30. S. Gjirja, E. Olsson, A. Karistrom, Considerations on engine design and fuelling technique effects on qualitative combustion in alcohol diesel engines, SAE Technical Paper Series 982530 31. K. Schmidt, J.H. Van Gerpen, The effect of biodiesel fuel composition on diesel combustion and emissions, SAE paper 961086 (1996) 32. W.G. Wang, D.W. Lyons, N.N. Clark, M. Gautam, P.M. Norton, Emissions from nine heavy trucks fuelled by diesel and biodiesel blend without engine modification. Environ. Sci. Technol. 34(6), 933–939 (2000) 33. A.L. Boehman, J. Song, M. Alam, Impact of biodiesel blending on diesel soot and the regeneration of particulate filters. Energy Fuels 19, 1857–1864 (2005) 34. M. Zheng, G.T. Reader, J.G. Hawley, Diesel engine exhaust gas recirculation—a review on advanced and novel concepts. Energy Convers. Manage. 45, 883–900 (2004) 35. D. Agarwal, S. Sinha, A. Kumar, Experimental investigation of control of NOx emissions in biodiesel-fueled compression ignition engine. https://doi.org/10.1016/j.renene.2005.12.003 36. M.J. Abedin, H.H. Masjuki, M.A. Kalam, A. Sanjid, S.M.A. Rahman, I.M.R. Fattah, Performance, emissions, and heat losses of palm and jatropha biodiesel blends in a diesel engine. Ind. Crops Prod. 59, 96–104 (2014) 37. A. Lobo, D.K. Ramesh, D.R. Chowdhury, M. Aditya, Experimental investigation on influence of additives on emissions, combustion and performance of diesel engine along with EGR fueled with waste cooking oil derived biodiesel. Int. J. Emer. Technol. 10(1), 01–03 38. V. Pradeep, R. Sharma, Use of HOT EGR for NOx control in a compression ignition engine fuelled with bio-diesel from Jatropha oil. Renew. Energy 32, 1136–1154 (2007)

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39. E.W. De Menezes, R. Da Silva, R. Cataluna, R.J.C. Ortega, Effect of ethers and ether/ethanol additives on the physicochemical properties of diesel fuel and on engine tests. Fuel 85, 815–822 (2006) 40. B.S. Chauhan, N. Kumar, H.M. Cho, A study on the performance and emission of a diesel engine fueled with Jatropha biodiesel oil and its blends. Energy 37(1), 616–622 (2012) 41. M. Gürü, A. Koca, Ö. Can, C. Cinar, F. Sahin, Biodiesel production from waste chicken fat based sources and evaluation with Mg based additive in a diesel engine. Renew. Energy 35, 637–643 (2010) 42. G. Knothe, A.C. Matheaus, T.W. Ryan, Cetane numbers of branched and straight chain fatty esters determined in an ignition quality tester. Fuel 82, 971–975 (2003) 43. M.J. Ramos, C.M. Fernandez, A. Casas, L. Rodriguez, A. Perez, Influence of fatty acid composition of raw materials on biodiesel properties. Bioresour. Technol. 100, 261–268 (2009) 44. M. Mittelbach, C. Remschmidt, Biodiesel the Comprehensive Handbook (Boersedruck Ges.m.b.H, Viena, 2004) 45. M. Lapuerta, J. Rodríguez-Fernández, E.F. De Mora, Correlation for the estimation of the cetane number of biodiesel fuels and implications on the iodine number. Energy Policy 37, 4337–4344 (2009) 46. D.Y. Chang, J.H. Van Gerpan, Fuel properties and engine performance for biodiesel prepared from modified feed stocks, SAE Paper 2005:2005–01–2200, https://doi.org/10.4271/971684 47. M.J. Murphy, J.D. Taylor, R.L. McCormick, Compendium of experimental cetane number data, NREL Report 2004: NREL/SR-540-36805 48. M.S. Graboski, J.R. Alvarez, R. McCormick, NOx solution for biodiesel, 2003:NREL/ SR510-31465 49. G.A. Ban-Weiss, J.Y. Chen, B.A. Buchholz, R.W. Dibble, A numerical investigation into the anomalous slight NOx increase when burning biodiesel: a new [old] theory. Fuel Process. Technol. 88(7), 659–677 (2007) 50. N. Ladommatos, R. Balian, R. Cooper, The effect of exhaust gas recirculation on soot formation in a high-speed direct-injection diesel engine, SAE Technical Paper No. 960841

Structural Modelling of Hydrocarbons for the Prediction of Octane Number and Designing of Sustainable Synthetic Fuel Sanjay Kumar, Mamta Thakur, Naman Shah, and Sarthak Jain

Abstract The objective of the present study is to develop the regression-based mathematical model in order to demonstrate the structural impact of the hydrocarbons on the octane number. To achieve this goal, a set of 66 (training set of 41 hydrocarbons and test set of 25 hydrocarbon) hydrocarbons has been considered and encoded into their structural descriptors, viz., Wiener Index (W), zero-order, first-order and second-order connectivity index (χ0, χ1, χ2), Schultz molecular topological index (SMTI), Balaban branching index (J) and indicator parameter (Ic). The multiple linear regression analysis (MLR) has been performed to obtain the structure–property relationship in the form of mathematical model. Moreover, the same model has also been used to predict the octane numbers of all the hydrocarbons. The model demonstrates the pivotal role of Schultz molecular topological index (SMTI), Balaban branching index (J), connectivity indices and indicator parameter in regulating the octane number of the hydrocarbons. In addition to the statistical parameters, the predictive ability and robustness of the model is further cross-validated by an external validation method by applying the model on the test set of 25 hydrocarbons. The proposed Quantitative Structure Property Relationship (QSPR) model provides a valuable insight to design the novel synthetic fuel that can be a sustainable energy solution for the future. Keywords Fuel, QSPR · Hydrocarbons · Octane number · Modelling

1 Introduction It is a well-known fact that the knocking property of the fuel and hence the octane rating of the fuel is a function of the molecular structure of hydrocarbons [1]. Octane S. Kumar · M. Thakur (B) · N. Shah · S. Jain School of Automobile and Manufacturing Engineering, Symbiosis University of Applied Sciences, Indore, M.P, India e-mail: [email protected]; [email protected] S. Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 C. S. Ramesh et al. (eds.), Recent Trends in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-2086-7_2

19

20

S. Kumar et al.

number (ON) is a figure-of-merit representing the resistance of gasoline to premature detonation when exposed to heat and pressure in the combustion chamber of an internal combustion engine [2]. It is measured against a prescribed binary mixture of isooctane (2,2,4trimethylpentane, ON = 100) and n-heptane (ON = 0) under standard conditions. The relationship between the molecular structure and octane number already attended certain basic set of rules, like ON increases with the number of tertiary and quaternary carbon atoms [3–7], it increases with the number of methyl groups [8–10], it decreases with the total number of carbon atom along the chain and ON increases with the shift of branch towards the centre of the longest carbon chain. In the present study certain additional structural features were identified to add value to the structural studies of the hydrocarbons. It is a well-known fact that the physical and chemical properties of a compound are a function of its molecular structure. Quantitative structure–property relationship (QSPR) is an empirically defined relationship between the molecular structure and the observed properties of the compounds and is represented as Property = f (structural descriptors) The structural descriptors tested in the present study are: Wiener index (W) [11], zero-order connectivity index (χ0) [12], first-order connectivity index (χ1) [13], second-order connectivity index (χ2) [14], Schultz molecular connectivity index (SMTI) [15], Balaban J index (J) [16], with an indicator parameter Ic (possesses value 1 if cyclic structure is present in the molecule; otherwise possesses 0). The topological descriptors were considered in the present study due to the direct physical significance of molecular size and shape in the octane number of the hydrocarbons. With these descriptors and experimental octane number, efforts have been made to develop a quantitative structure–property relationship (QSPR) model expedient, to explore structural impact, to predict octane number, and finally to design a molecule(s) exhibiting desirable octane number. The multiple linear regression method has been employed to establish predictive quantitative structure–property relationship (QSPR) model, which reflects the effect of structural features on which octane number of the hydrocarbon relies. Also, this model is useful to predict the octane number of the hydrocarbons that do not belong to a given set of compounds. The work has a potential to provide a significant inputs in the area of structural modelling of the hydrocarbons with the desirable octane number.

2 Computational Methodology Experimental data of octane number for the data set of 66 compounds were taken from the literature [17]. The dataset of 66 compounds has been classified as training set of 41 compounds and test set of 25 compounds. The molecular structures were drawn

Structural Modelling of Hydrocarbons for the Prediction …

21

and 3D optimized using ACD Chemsketch software and the structural descriptors for each molecule have been calculated using E-dragon (java-based program). The calculated descriptors, viz., Wiener index (W), zero-order connectivity index (χ0), first-order connectivity index (χ1), second-order connectivity index (χ2), Schultz molecular topological index (SMTI), Balaban J index (J) and indicator parameter (Ic ) to indicate the presence and absence of cyclic group in a compound were listed in Table 1 for training as well as for the test set of the hydrocarbons. The selection of structural descriptor has been done by multiple linear regression method using SPSS software. Octane number has been classified as a dependent parameter and rest all descriptors were classified as independent parameters in variable selection step of regression analysis in SPSS. Only training set was used to develop QSPR model, whereas test set is used for the external validation of the QSPR model. The correlatedness between all the descriptors and octane number is shown in Table 2. Step-up multiple linear regression (MLR) method has been adopted using SPSS software for the selection of the descriptors which regulates the octane number of the hydrocarbons. MLR analysis subsequently leads to the mathematical model which shows quantitative relationship between the selected descriptors and octane number. In the final step, the octane numbers of the hydrocarbons in a training and a test set have been predicted using QSPR model obtained in the MLR analysis.

2.1 Validation of the Model The validation of QSPR model (Eq. 1) has been performed on the basis of two strategies: (i) Internally validated by the statistical parameters shown below the Eq. (1) and (ii) Property prediction of test set compounds. In general, r2 of the test set greater than 0.6 represents good prognostic ability of the model [18].

3 Results The mathematical model obtained from the MLR analysis using SPSS has been represented in Eq. (1). ON = 0.125 SMTI (±0.125) + 49.9 J (±11) + 70.7 χ0 (±21.1) − 143.8 χ1(±27.99) − 31.89 χ2 (±12.5) + 109.533 Ic (±15.6) + 35.02

(1)

N = 41, R = 0.946, R2 = 0.894, adjusted R2 = 0.875, standard error of estimate = 9.4, predicted residual sum of squares (PRESS) = 2893.177 and sum of square of Y (SSY) = 27361.010, PRESS/SSY = 0.106.

2,3-Dimethylpentane

Methylcyclopentane

1,2,3-Trimethylcyclopentane

3-Ethyl,2-methylpentane

2,4-Dimethylpentane

14.

15.

16.

2,2-Dimethylpentane

8.

13.

Methylbutane

7.

12.

2,2,4 Trimethylpentane

6.

2,2-Dimethylbutane

Cyclopentane

5.

11.

Methylpropane

4.

1,1,2,4-Tetramethylcyclopentane

2,3,4-Trimethylpentane

3.

1,1-Dimethylcyclopentane

2,3-Dimethylbutane

2.

10.

2,2,3-Trimethylpentane

1.

9.

Compound name

S. No.

Training set of hydrocarbons

176

242

252

126

168

106

180

334

170

68

342

80

36

236

108

230

SMTI

48

67

58

26

46

28

39

79

46

18

94

15

9

65

29

63

W

2.953

3.355

2.436

2.184

3.144

3.168

2.4

2.583

3.154

2.54

3.467

2.083

2.324

3.464

2.993

3.623

J

5.862

6.569

6.146

4.406

5.862

5.207

5.328

7.069

5.914

4.284

7.492

3.536

3.577

6.732

5.155

6.784

χ0

Table 1 Training set and test set of hydrocarbons with their structural descriptors and indicator parameter

3.126

3.719

3.715

2.894

3.181

2.561

3.207

4.022

3.061

2.27

3.955

2.5

1.732

3.553

2.643

3.481

χ1

3.023

2.821

3.391

2.39

2.63

2.914

3.371

4.435

3.311

1.802

4.278

1.768

1.732

3.347

2.488

3.675

χ2

0

0

1

1

0

0

1

1

0

0

0

1

0

0

0

0

(continued)

Indicator

22 S. Kumar et al.

Compound name

1,1,3-Trimethylcyclopentane

Isopropylcyclopentane

3,3-Dimethylpentane

3-Ethyl,3-methylpentane

1,3-Dimethylcyclopentane

3,4-Dimethylhexane

3,3-Dimethylhexane

3-Methylpentane

2-Methylpentane

2,2-Dimethylhexane

2,3-Dimethylhexane

Ethylcyclopentane

2,4-Dimethylhexane

n-Pentane

2-Ethyl,1-methylcyclopentane

2,5-Dimethylhexane

3-Methylhexane

2-Methylhexane

Isobutylcyclopentane

n-Proplycyclopentane

S. No.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

Training set of hydrocarbons

Table 1 (continued)

290

394

190

182

270

264

74

258

194

254

260

118

114

244

246

186

232

162

270

254

SMTI

67

93

52

50

64

61

20

71

43

70

71

32

31

67

68

41

64

44

62

58

W

2.058

2.113

2.678

2.832

2.928

2.303

2.191

3.099

2.141

3.171

3.112

2.627

2.754

3.373

3.292

2.257

3.583

3.36

2.242

2.435

J

5.81

6.69

5.699

5.699

6.569

5.983

4.121

6.569

5.113

6.569

6.621

4.992

4.992

6.621

6.569

5.276

6.621

5.914

5.983

6.199

χ0

3.932

4.288

3.27

3.308

3.626

3.843

2.414

3.664

3.432

3.681

3.561

2.77

2.808

3.621

3.719

3.288

3.682

3.121

3.805

3.601

χ1

2.939

3.78

2.536

2.302

3.365

3.077

1.354

3.143

2.559

3.01

3.664

2.183

1.922

3.268

2.771

3.023

2.871

2.871

3.289

4.012

χ2

1

1

0

0

0

1

0

0

1

0

0

0

0

0

0

1

0

0

1

1

(continued)

Indicator

Structural Modelling of Hydrocarbons for the Prediction … 23

n-Hexane

3-Methylheptane

4-Methylheptane

2-Methylheptane

n-Heptane

37.

38.

39.

40.

41.

2,2-Dimethylheptane

Diethylpentane

2,2-Dimethyl-3-ethylpentane

2,4-Dimethyl-3-ethylpentane

2,2,3,3-Tetramethylpentane

3,3,4-Trimethylheptane

2,2,3,3-Tetramethylhexane

Cyclohexane

Methylcyclohexane

Ethylcyclohexane

42.

43.

44.

45.

46.

47.

48.

49.

50.

51.

Test set of hydrocarbons

Compound name

S. No.

Training set of hydrocarbons

Table 1 (continued)

280

193

132

416

444

298

324

318

316

380

204

288

272

276

128

SMTI

64

42

27

115

123

82

90

88

92

104

56

79

75

76

35

W

2.125

2.123

2

4.282

3.778

4.145

3.678

3.793

3.825

3.073

2.447

2.716

2.92

2.862

2.339

J

5.82

5.113

4.243

8.414

8.199

7.707

7.439

7.492

7.328

7.328

5.536

6.406

6.406

6.406

4.828

χ0

3.932

3.394

3

4.371

4.542

3.811

4.091

4.019

4.243

4.061

3.414

3.77

3.808

3.808

2.914

χ1

2.912

2.743

2.121

4.475

4.032

4.487

3.56

3.879

2.914

4.018

2.061

2.89

2.683

2.656

1.707

χ2

1

1

1

0

0

0

0

0

0

0

0

0

0

0

0

(continued)

Indicator

24 S. Kumar et al.

Compound name

1,1-Dimethylcyclohexane

1,2-Dimethylcyclohexane

1,3-Dimethylcyclohexane

1,4-Dimethylcyclohexane

n-Propylcyclohexane

Isopropylcyclohexane

1-Methyl-1-ethylcyclohexane

1,1,2-Trimethylcyclohexane

1,2,3-Trimethylcyclohexane

1,2,4-Trimethylcyclohexane

1,3,5-Trimethylcyclohexane

Isobutylcyclohexane

sec-Butylcyclohexane

1-Isopropyl-4-methylcyclohexane

1-Methyl-2-n-propylcyclohexane

S. No.

52.

53.

54.

55.

56.

57.

58.

59.

60.

61.

62.

63.

64.

65.

66.

Training set of hydrocarbons

Table 1 (continued)

502

498

504

526

357

357

349

343

359

375

399

272

268

264

262

SMTI

121

120

121

126

84

84

82

80

84

88

94

62

61

60

59

W

2.251

2.26

2.24

2.131

2.341

2.346

2.413

2.491

2.366

2.228

2.078

2.192

2.231

2.279

2.328

J

7.397

7.56

7.397

7.397

6.853

6.853

6.853

6.906

6.743

6.69

6.527

5.983

5.983

5.983

6.036

χ0

4.843

4.698

4.843

4.788

4.182

4.198

4.215

4.128

4.268

4.305

4.432

3.788

3.788

3.805

3.707

χ1

3.81

4.264

3.784

4.134

4.023

3.873

3.745

4.135

3.725

3.642

3.293

3.365

3.377

3.239

3.725

χ2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Indicator

Structural Modelling of Hydrocarbons for the Prediction … 25

26

S. Kumar et al.

Table 2 Correlation matrix representing intercorrelatedness between the descriptors ON Pearson correlation

x0

x1

x2

1.000

−0.391

0.275

−0.175

−0.402

0.163

−0.391

1.000

0.123

0.875

0.972

0.764

0.269

0.275

0.123

1.000

0.567

0.155

0.270

−0.696

x0

−0.175

0.872

0.567

1.000

0.872

0.789

−0.101

x1

−0.402

0.972

0.155

0.872

1.000

0.709

0.273

x2

0.163

0.764

0.270

0.789

0.709

1.000

0.336

Indicator

0.090

0.269

−0.696

−0.101

0.273

0.336

1.000

ON SMTI J

SMTI

J

Indicator 0.090

The internal validation of the mathematical model Eq. (1) has been done by investigating the statistical parameters shown below the equation, viz., regression coefficient (R), R2 , standard error of estimation and F-ratio. The developed models were further validated by the calculation of the following statistical parameters: predicted residual sum of squares (PRESS), total sum of squares deviation (SSY) and cross-validated correlation coefficient (r2 adj). PRESS [19, 20] is an important cross-validation parameter as it is a good approximation of the real predictive error in the model. Its value being less than SSY points out that the model predicts better chance and can be considered statistically significant. The smaller PRESS value means better model predictability. Also, for reasonable QSPR model, the PRESS/SSY ratio should be lower than 0.4 [21]. The data presented in Eq. (1) indicate that for the developed model this ratio is 0.106. The predicted octane number for the training set of 41 compounds is given in Table 3 along with the experimental octane number, unstandardized residues and standardized residues. For the graphical visualization of outlier, the Williams plot has been shown in Fig. 1. The difference between the experimental ON and predicted ON is depicted as unstandardized residuals in Table 3, whereas the residual divided by an estimate of its standard deviation is depicted as standardized residuals [22] in Table 3. Standardized residuals quantify how large the residuals are in standard deviation units, and therefore can be easily used to identify outliers. It is an assumption in the statistics that the data showing standardized residue within the ± 3 values will not be considered as outlier or a misfit data in the set [23]. It is worthy to show the standardized residue in the present study, since some of the predicted octane number shows deviation from experimental octane number by approximately 10 units or more. The reason for high difference is the usage of real data (data without normalization) of octane number in the QSPR analysis, and this subsequently leads to high values of regression coefficient of independent parameters, e.g., 49.9 for J, 70.7 for χ0, 143.8 for χ1, 31.89 for χ2, and 109.533 for Ic , this will bring an abrupt change in the predicted octane number on the unit increment in the independent parameter. In order to cope up with this misleading information due to unstandardized residue, the fitness of the data has been ensured on the basis of standardized residual values.

Compound name

2,2,3-Trimethylpentane

2,3-Dimethylbutane

2,3,4-Trimethylpentane

Methylpropane

Cyclopentane

2,2,4-Trimethylpentane

Methylbutane

2,2-Dimethylpentane

1,1,2,4-Tetramethylcyclopentane

1,1-Dimethylcyclopentane

2,2-Dimethylbutane

2,3-Dimethylpentane

Methylcyclopentane

1,2,3-Trimethylcyclopentane

3-Ethyl,2-methylpentane

2,4-Dimethylpentane

1,1,3-Trimethylcyclopentane

Isopropylcyclopentane

3,3-Dimethylpentane

S. No.

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

80.8

81.1

81.7

83.1

87.3

89.2

89.3

91.1

91.8

92.3

92.6

92.8

93

100

101.6

102.1

102.7

104.3

109.6

Experimental octane no.

101.61

61.21

91.30

73.80

73.20

88.42

87.69

85.69

115.56

95.16

97.02

88.32

88.91

82.38

93.19

106.42

97.41

103.02

109.50

Predicted octane no.

2.01 −2.11

19.89 −20.81

0.94 −0.97

9.30

1.43

0.08

0.16

−9.60

14.10

0.78

1.61

0.55

−0.29 −2.40

−2.86 −23.76 5.41

0.45 −0.45

−4.42

0.41

1.78

0.85

4.48

4.09

17.62

8.41

0.54 −0.44

5.29

0.13

0.01

(continued)

Standardized residue

−4.32

1.28

0.10

Unstandardized residues

Table 3 Experimental Octane number, predicted octane number, unstandardized residues and standardized residue for the training set of 41 hydrocarbons

Structural Modelling of Hydrocarbons for the Prediction … 27

Compound name

3-Ethyl,3-methylpentane

1,3-Dimethylcyclopentane

3,4-Dimethylhexane

3,3-Dimethylhexane

3-Methylpentane

2-Methylpentane

2,2-Dimethylhexane

2,3-Dimethylhexane

Ethylcyclopentane

2,4-Dimethylhexane

n-Pentane

2-Ethyl,1-methylcyclopentane

2,5-Dimethylhexane

3-Methylhexane

2-Methylhexane

Isobutylcyclopentane

n-Proplycyclopentane

n-Hexane

3-Methylheptane

4-Methylheptane

2-Methylheptane

n-Heptane

S. No.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

40.

41.

Table 3 (continued)

0

21.7

26.7

26.8

31

31.2

33.4

46.4

52

55.5

57.6

61.8

65.2

67.2

71.3

72.5

73.4

74.5

75.5

76.3

79.9

80.8

Experimental octane no.

16.56

27.96

36.23

34.58

33.31

35.87

39.80

44.13

52.01

53.65

63.74

52.41

61.32

60.49

65.72

66.09

64.90

72.86

79.58

71.92

83.21

90.94

Predicted octane no.

−0.47 −0.23 −0.79 −0.96 −0.63 −1.68

−2.31 −7.78 −9.53 −6.26 −16.56

−0.65

−6.40 −4.67

0.23

2.27

0.19 0.00

−0.01

−0.62

−6.14 1.85

0.95

0.39

0.68

0.56

0.65

0.86

0.17

9.39

3.88

6.71

5.58

6.41

8.50

1.64

0.44 −0.41

4.38

−0.34

−3.31 −4.08

Standardized residue −1.03

Unstandardized residues −10.14

28 S. Kumar et al.

Structural Modelling of Hydrocarbons for the Prediction …

29

140.00

Predicted Octane No.

120.00 100.00 80.00 60.00 40.00 20.00 0.00 0

20

40

60

80

100

120

Experimental Octane No.

Fig. 1 Correlation between experimental and predicted Octane no of training set

4 Discussion and Interpretation of QSPR Model Quantitative structure–property relationship provides useful insight about the dependence of properties of molecules on its structure. The structural aspects that largely affect octane number were selected by MLR method, but the interpretation part of these descriptors actually reveals their importance. In the present study, SMTI plays a positive role in regulating octane number of hydrocarbons. Let  be a molecular graph on N vertices. The “molecular topological index” (MTI) of the graph  introduced by Schultz in 198915 is defined in the following way: MTI = MTI(Γ ) =i = 1

N 

[v(A + D)]I

(2)

Equation 2 comprises summation of adjacency matrix (A) and distance matrix (D) of the compound, which in turn is a multiple of valencies (v) of the vertices in the graph. The positive coefficient of SMTI indicates that higher SMTI is required in the compound to increase its octane number, but the magnitude of coefficient is very low, which leads to the inference that out of the three component participating in the SMTI descriptor (i.e., adjacency, distance vector and valencies of vertices), optimization of either one or two components is required. Another descriptor included in Eq. (1) is Balaban J index (J) that effectively discriminates cyclic and acyclic structures and branching in the structure. Positive coefficient of J with high magnitude indicates branched cyclic structure exhibits highest octane number, then unbranched cyclic structure which in turn possesses higher octane number than their acyclic or linear hydrocarbon analogues. The descending order of octane number of hydrocarbon with reference to J index is expected as:

30

S. Kumar et al.

Branched cyclic analogue > cyclic Analogue > Branched acyclic > n-hydrocarbon analogue. By the virtue of J it has been observed that adjacency and valencies of the atoms in hydrocarbons is required to be higher as compared to distances between the atoms. Inclusion of J with SMTI optimized three components of SMTI and also justified lower coefficient of SMTI. Zero-order Randi´c connectivity index and the values of χ0 index increase with the increase in length and branches of hydrocarbon chains. This descriptor is a vertexbased descriptor and is applicable throughout the molecule. It is represented in Eq. (3) i = n  √ χ0 = 1/ δi i = 1

(3)

where χ0 = zero-order connectivity index, n = total number of vertices in the molecular graph δ = valance of carbon atom in hydrogen suppressed molecular graph. χ0 is a vertex weighted connectivity index. Zero-order connectivity index gives an information about the chain length and branching in two dimensions. Positive coefficient of χ0 shows higher value of this descriptor will increase the octane number. First-order Randi´c connectivity index χ1 is an edge-weighted descriptor expressed as No of edges or bonds χ1 =



i = 1

√ 1/ δi×δj (4)

χ1 contains information about the molecular volume and molecular surface area. Higher value of χ1 indicate less molar volume and low surface area of the molecule, and subsequently reduces octane number. χ1 in the present study significantly reflects all the specifications of branching present in a hydrocarbon. The length of the branch should not extend beyond a certain limit. For this reason most of the fuels were methyl substituents and not ethyl, propyl, butyl and so on. Below are the two examples, Figs. 2 and 3, demonstrating the inverse relationship of χ1 with molar volume and octane number. χ1 descriptor differentiates chain isomers in terms of octane number. A chain isomer possessing lower χ1 value possesses high volume and surface area which subsequently leads to a high octane rating. Second-order connectivity index χ2 has been represented as:

Structural Modelling of Hydrocarbons for the Prediction …

Fig. 2 2-methyl pentane (χ1 = 2.77, molar volume = 127.9 cm3 , Octane no = 46.4)

Fig. 3 2,3, di methyl butane (χ1 = 2.643, molar volume = 128.3, Octane no = 104.3)

31

32

S. Kumar et al.

No of edges or bond − 1  √ χ2 = 1/ δiδjδk i=1

(5)

χ2 is derived from fragment of two bond lengths and hence provides information about position and type of branching. It also indicates the structural flexibility of the molecule. It is more suitable to compare positional isomerism in hydrocarbon. As per the QSPR model obtained and shown in Eq. (1), χ2 is with negative coefficient, thus signifying the inverse relationship between χ2 and octane rating. Molecule with higher χ2 will show lower octane rating, e.g., 2-methyl pentane having χ2 = 2.183 and octane rating 73.4; and 3-methyl pentane with χ2 = 1.992 showing octane rating = 74.5. This supports the hypothesis that shifting of branch to the centre of the molecule favours octane rating, and the presence of branch on the right or left extremes of molecule reduces octane rating. Indicator parameter Ic is used in the present study to highlight the significance of cyclic structure in the molecule. The positive coefficient of Ic supports the presence of cyclic structure in a molecule with reference to octane number. The predicted octane number of training set of 41 compounds using Eq. (1) has been shown in Table 3.

4.1 Validation Set External cross-validation of the model has also been done by applying the QSPR model obtained as Eq. (1) on the test set of 25 compounds. All the independent variables of 25 compounds, viz., SMTI, J, χ0, χ1, χ2 and Ic were calculated and shown in Table 1, and their corresponding predicted and experimental octane numbers are shown in Table 4. The linear correlation between experimental and predicted octane numbers is graphically represented in Fig. 4 with all statistical parameters. Equation (1) has also been validated by the test set which is characterized by the statistical summary of the predicted and experimental octane number with R = 0.931, R2 = 0.866, adjusted R2 = 0.86, standard error of estimate = 9.6. The statistical parameter obtained for the test set of 25 compound further validates the predictive ability of the QSPR model.

5 Conclusions The QSPR model obtained in the present study reveals the importance of structural features on the octane number of the hydrocarbons. The presence of SMTI in a model with a positive coefficient leads to the inference that the hydrocarbon with optimum chain length possessing carbon in its at-most valency will exhibit high octane number.

Structural Modelling of Hydrocarbons for the Prediction …

33

Table 4 Experimental and predicted octane number of test set using Eq. (1) S. No.

Compounds

1.

2,2-Dimethylheptane

Octane no.

2.

Diethylpentane

84.00

80.41

3.

2,2-Dimethyl-3-ethylpentane

112.10

92.09

4.

2,4-Dimethyl-3-ethylpentane

105.30

83.18

5.

2,2,3,3-Tetramethylpentane

116.80

132.88

6.

3,3,4-Trimethylheptane

7.

2,2,3,3-Tetramethylhexane

8.

Cyclohexane

84.00

61.79

9.

Methylcyclohexane

73.80

60.57

10.

Ethylcyclohexane

46.50

38.78

11.

1,1-Dimethylcyclohexane

87.30

68.36

12.

1,2-Dimethylcyclohexane

80.90

63.82

13.

1,3-Dimethylcyclohexane

69.30

59.97

14.

1,4-Dimethylcyclohexane

67.70

58.91

15.

n-Propylcyclohexane

17.80

17.24

16.

Isopropylcyclohexane

62.80

40.39

17.

1-Methyl-1-ethylcyclohexane

68.70

51.69

18.

1,1,2-Trimethylcyclohexane

95.70

74.51

19.

1,2,3-Trimethylcyclohexane

84.80

67.55

20.

1,2,4-Trimethylcyclohexane

72.90

63.57

21.

1,3,5-Trimethylcyclohexane

63.80

60.84

22.

Isobutylcyclohexane

33.70

19.26

23.

sec-Butylcyclohexane

51.00

25.20

24.

1-Isopropyl-4-methylcyclohexane

67.30

42.52

25.

1-Methyl-2-n-propylcyclohexane

29.90

24.67

50.30

Predicted octane no. 41.85

86.40

76.99

112.80

122.30

Also, the positive coefficient of Balaban J Index reflects increase in octane number with the presence of branched cyclic structures. The positive coefficient of χ0 indicates that with increase in chain length there will be an increase in octane number. Also, it supports the effect of branching on the octane number of hydrocarbon. Negative coefficient of χ1 shows that with increase in χ1 there will be a decrease in the octane number, since χ1 is a function of molar volume and molecular surface area, and it has also been found that lower the surface area or molecular volume greater will be the χ1. Therefore, the negative coefficient of χ1 indicates, lower values of χ1 will increase the octane number of hydrocarbons. χ1 descriptor differentiates chain isomers in terms of octane number. A chain isomer with lower χ1 value possesses high volume and surface area which subsequently leads to a high octane rating. As per the QSPR model obtained as in Eq. (1), χ2 possesses negative coefficient, thus

34

S. Kumar et al. 140.00

Predicted Octane No

120.00 100.00 80.00 60.00 40.00 20.00 0.00 0

20

40

60

80

100

120

140

Experimental Octane No

Fig. 4 The predictive ability of the model Eq. (1)

signifying the inverse relationship between χ2 and octane rating. It supports in differentiating positional isomers of hydrocarbon with reference to octane number, and it has been found that isomer with higher positioning of branch shows higher octane number.

References 1. W. Leppard, The chemical origin of fuel octane sensitivity. SAE Trans. J. Fuel Lubricants 99(4), 862–876 (1990) 2. V. Mittal, J.B. Heywood, The shift in relevance of fuel RON and MON to knock onset in modern SI engines. SAE Int. J. Eng. 2(2), 1–10 (2010) 3. https://www.cpp.edu› ~ psbeauchamp›pdf›314_supp_6_isom_form 4. V.L. Tal’roze, A.L. Lyubimova, Dokl. Akad. Nauk SSSR 86, 909 (1952); Chem. Abstr. 47, 2590 (1953) 5. S. Wexler, N. Jesse, J. Am. Chem. Soc. 84, 3425 (1962) 6. A.L.L. East, Z.F. Liu, C. McCague, K. Cheng, J.S. Tse, J. Phys. Chem. A 102, 10903 (1998) 7. K.C. Hunter, A.L.L. East, J. Phys. Chem. A 106, 1346 (2002) 8. J. Warnatz, in Combustion: Physical and Chemical Fundamentals, Modeling and Simulation, Experiments, Pollutants Formation, ed. by J. Warnatz, U. Maas, R.W. Dibble, 2nd edn. (Springer, Berlin, 1999) 9. W. Lovell, Ind. Eng. Chem. 40, 2388–2438 (1948) 10. C. Morley, Combust. Sci. Technol. 55, 115–123 (1987) 11. H. Wiener, J. Am. Chem. Soc. 1(69), 17–20 (1947) 12. M. Randi´c, On characterization of molecular branching. J. Am. Chem. Soc. 97, 6609–6615 (1975) 13. M. Randi´c, On history of the Randi´c index and emerging hostility toward chemical graph theory MATCH commun. Math. Comput. Chem. 59, 5–124 (2008) 14. L.B. Kier, L.H. Hall, Molecular Connectivity in Chemistry and Drug (Research Academic Press, New York, 1976)

Structural Modelling of Hydrocarbons for the Prediction …

35

15. H.P. Schultz, J. Chem. Inf. Comput. Sci. 29, 227–228 (1989) 16. A.T. Balaban, Pure Appl. Chem. 55, 199–206 (1983) 17. J.H. Al-Fahemi, N.A. Albis, E.A.M. Gad, J. Theor. Chem. 2014(520652), 6. https://doi.org/ 10.1155/2014/520652 18. TSAR reference guide 19. M.T.D. Cronim, B.W. Gregory, J.W. Shutz, Chem. Res. Toxicol. 11, 902–912 (1998) 20. G.E.B. Box, W.G. Hunter, J.S. Hunter, Statistics for Experiments (Wiley, New York, 2000) 21. V.A. McNally, M. Rajabi, A. Gbaj, I.J. Stratford, P.N. Edwards, K.T. Douglas, R.A. Bryce, M. Jaffar, S. Freeman, J. Pharm. Pharmacol. 59, 537–538 (2007) 22. www.ibm.com 23. R.J. Rossi, Applied Biostatistics for the Health Sciences (Wiley, 2010)

Optimization of Wear Behavior of Aluminum–Boron Carbide Composites Using Factorial Analysis S. Sanman, K. P. Prashanth, and G. N. Lokesh

Abstract In recent industrial applications the utilization of monolithic materials is finding quite difficulty due to their untailored properties. In this concern newer composite materials are emerging day-by-day due to their tailored properties and benefits like low cost, high strength to low weight, and availability. In the present research work, the optimization of wear behavior of aluminum–boron carbide composites using factorial analysis has been studied. The composite was synthesized using stir casting route. Different variables like testing temperature, testing time and particle size of B4 C were selected to optimize the wear behavior of composite. The weight percentage of boron carbide was kept constant at 8 wt%. The tests were conducted by employing the pin-on-disc wear testing machine. After performing ANOVA and factorial analysis, it is noted that the wear rate increases with the increase in particle size and testing temperature for a given weight fraction of B4 C and decreases with the increase in the testing time. Further, it can be concluded that the factors, like particle size and the testing temperature, significantly affect the wear rate of the composite. Keywords Aluminum–Boron carbide composites · Pin-on-disc wear · Factorial analysis

S. Sanman (B) · K. P. Prashanth Department of Mechanical Engineering, Acharya Institute of Technology, Bengaluru, India e-mail: [email protected] K. P. Prashanth e-mail: [email protected] G. N. Lokesh Department of Mechanical Engineering, School of Engineering, Presidency University, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 C. S. Ramesh et al. (eds.), Recent Trends in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-2086-7_3

37

38

S. Sanman et al.

1 Introduction In the present scenario, aluminum metal matrix composites (AMMCs) have been the first choice of material for tribological and structural applications over conventional materials due to their extraordinary tailorable properties. The addition of hard ceramic reinforcement has made the aluminum metal matrix composites to create a major benchmark by being a better wear resistance material for marine, aerospace and automobile components at room and elevated temperatures [1–3, 11]. Effect of temperature, particle size, wt% of reinforcement on the dry sliding wear behavior of Al-B4 C composites has been studied and evaluated [3, 4]. Liquid metallurgy route has been adopted for the fabrication of AMMCs due to ease and low cost [5, 6]. Many researchers have evaluated and reported the effect of wt% of reinforcement and different particle sizes on the pin-on-disc dry sliding wear behavior of Al-B4 C composites at room temperature [7, 8]. The wear behavior of the composite material will vary according to application, working condition, temperature, particle size, reinforcement, load and other parameters. The analysis of parameter which has a major impact on the wear behavior of materials and also its optimization was achieved by using 33 factorial analysis [9, 10]. From the limelight of the above literature survey, the present work aims at identifying the variable or parameters which are affecting the wear properties of Al-B4 C composite and developing the structural relationship between the variables to identify key variables which affect the wear properties and finally each parameter is compared iteratively and best solution is adopted.

2 Experimental Procedure 2.1 Composite Preparation and Methodology Adopted A brief explanation regarding the preparation of mold and composite are discussed and reported in our previous published work [4]. Figure 1 shows the methodology adopted. Factorial arrangement is made at three levels with three factors, each referring to the factor levels as low, intermediate and high. There are three factors in this study, each factor being organized in a factorial analysis at 3 levels. The 27 varieties of combinations hold 26 degrees of freedom and each effect has 2 degrees of freedom and each has 4 degrees of freedom for every 2-factor interaction. The relationship of the three variables holds 8 degrees of freedom. If there are n, then the complete degrees of freedom (n33 1) and 33 (n1) are repeated. Further, ABC’s 3-factor interactions can be divided into four orthogonal 2 degrees of components, usually referred to as AB2 C2 , ABC2 and ABC interaction.

Optimization of Wear Behavior of Aluminum–Boron Carbide Composites … Fig. 1 Methodology adopted

39

Define the objectives Identify the Key variables and define the levels for each variable Design of experiment for one way ANOVA

Design the experiment for 33 Factorial

Collecting the data, fabrication of the composite and conducting the experiment

Analysis of variance by using mini tab

Results and Discussion

Table 1 Key variables and levels of each variable

Variables

Level-1

Level-2

Level-3

Time [minutes]

120

360

600

Temperature [°C]

25°

75°

150°

Particle size of B4 C [microns]

0µ

63 µ

105 µ

2.2 Identify Key Variables and Define the Level of Each Variable The key variable factors which affect the wear properties of Al-B4 C composites are chosen as time, temperature and particle size of B4 C. For each variable three levels were adopted (Table 1).

3 Results and Discussions 3.1 Data Collection and Experimentation The data of wear rate for different values of identified variables are tabulated in Table 2.

40

S. Sanman et al.

Table 2 Data collection of wear rate S. no.

Particle size of B4 C

Temperature

Time

Wear rate

1

63

75

120

0.98050

2

0

25

600

0.04731

3

0

25

600

0.04893

4

105

150

120

1.95540

5

63

25

120

0.45260

6

0

25

120

0.25020

7

0

75

600

0.73210

8

0

75

600

0.75832

9

0

25

360

0.09320

10

63

25

120

0.46389

11

63

150

600

0.64450

12

0

150

360

1.15300

13

63

25

360

0.32950

14

63

150

120

1.19540

15

0

150

600

0.93200

16

63

150

360

0.84440

17

105

25

600

0.32290

18

63

25

600

0.30050

19

63

75

120

0.95347

20

105

25

360

0.39120

21

0

25

120

0.25318

22

0

75

120

1.10680

23

0

75

600

0.71572

24

63

150

600

0.63682

25

105

150

360

1.11230

26

0

75

360

0.89650

27

105

75

120

1.78000

28

105

75

360

0.86000

29

105

75

600

0.67150

30

0

150

600

0.92897

31

105

150

120

1.98732

32

0

25

360

0.09289

33

0

150

120

1.32600

34

63

25

120

0.47562

35

63

25

600

0.29972

36

63

75

360

0.77800 (continued)

Optimization of Wear Behavior of Aluminum–Boron Carbide Composites …

41

Table 2 (continued) S. no.

Particle size of B4 C

Temperature

Time

Wear rate

37

105

25

120

0.53590

38

63

25

360

0.32381

39

105

75

120

1.77930

40

63

75

360

0.76193

41

0

25

360

0.09173

42

63

150

360

0.83974

43

105

25

360

0.39381

44

0

150

360

1.13972

45

105

75

600

0.67483

46

0

75

120

1.11029

47

105

150

600

0.89760

48

0

75

120

1.10372

49

105

25

600

0.31997

50

105

25

600

0.32857

51

105

150

360

1.11599

52

0

25

600

0.04654

53

0

75

360

0.89362

54

0

150

120

1.32549

55

63

75

120

0.97781

56

105

75

360

0.86043

57

105

25

120

0.53179

58

105

25

120

0.53321

59

0

75

360

0.88732

60

0

150

120

1.32181

61

105

75

360

0.85870

62

105

25

360

0.39184

63

0

150

600

0.93371

64

63

75

600

0.56780

65

63

150

120

1.19427

66

105

75

600

0.67231

67

0

150

360

1.15219

68

63

150

360

0.84119

69

105

75

120

1.78119

70

63

75

600

0.55924

71

63

25

600

0.30159

72

63

75

600

0.56193 (continued)

42

S. Sanman et al.

Table 2 (continued) S. no.

Particle size of B4 C

Temperature

Time

Wear rate

73

105

150

120

1.93610

74

63

150

600

0.64139

75

63

25

360

0.31349

76

105

150

360

1.11893

77

63

75

360

0.77214

78

105

150

600

0.88241

79

0

25

120

0.24987

80

105

150

600

0.89024

81

63

150

120

1.19513

Table 3 Factor information for 33 factorial

Factor

Levels

Values

Particle size

3

0, 63, 105

Temperature

3

25, 75, 150

Time

3

120, 360, 600

3.2 Factor Information See Table 3.

3.3 Analysis of Variance (ANOVA) See Table 4. Table 4 Analysis of variance for 33 factorial Source

DF

Adj. SS

Adj. MS

F-value

P-value

Particle

2

1.1389

0.56947

8259.45

0.000

Temperature

2

9.7863

4.89317

70,969.08

0.000

Time

2

3.5284

1.76421

25,587.55

0.000

Particle*temp

4

0.6228

0.15569

2258.15

0.000

Particle*Time

4

0.7737

0.19342

2805.37

0.000

Temp*Time

4

0.6569

0.16422

2381.85

0.000

Particle*Temp*Time

8

0.4030

0.05037

730.59

0.000

Error

54

0.0037

0.00007

Total

80

16.9138

Optimization of Wear Behavior of Aluminum–Boron Carbide Composites … Table 5 Model summary

R-sq

R-sq (adj)

99.98%

99.97%

43

3.4 Model Summary See Table 5.

3.5 Regression Equation Wear rate = 0.782485 − 0.05689 Particle_0 − 0.10817 Particle_63 + 0.16506 Particle_105 −0.47938 Temp_25 + 0.14550 Temp_75 + 0.33389 Temp_150 + 0.28256 Time_120 −0.06739 Time_360 − 0.21517 Time_600 − 0.11579.

4 Graphs 4.1 Residual Plot for Wear Rate The normality of population can be verified with a regular plot of residual probability. If the residual distribution is normal the plot will look like a straight line. The graph in Fig. 2 follows the essential principles of experimental design. The residual normality plot above shows the residuals follow a normal distribution. Both residual plot versus fitted values and residual plot versus run order show no pattern. Therefore, it satisfies both the constant uncertainty and the guarantees of freedom.

4.2 Main Effects Plot for Wear Rate The graph in Fig. 3 represents that the wear rate for the composite with B4 C of particle size 105 µ is around 0.95, which is higher than the wear rate of the composite with B4 C of particle size 63 µ and the base alloy. The following details can be attributed to the increase in wear rate with increase in particle size for a given percentage of 8 wt% boron carbide reinforcement. As the particle size increases the number of B4 C particles decreases for the given weight percentage of reinforcement. Thus the increase in the particle size decreases the number of B4 C particles dispersed in the

44

S. Sanman et al.

Residual Plots for Wear RATE Normal Probability Plot

Versus Fits

99.9

0.030

99

0.015

Residual

Percent

90 50 10

0.000 -0.015

1

-0.030

0.1

-0.030

-0.015

0.000

0.015

0.030

0.0

0.5

1.0

Histogram

Versus Order 0.015

36

Residual

Frequency

2.0

0.030

48

24

0.000 -0.015

12 0

1.5

Fitted Value

Residual

-0.030 -0.024

-0.012

0.000

0.012

1

0.024

10

20

30

40

50

60

70

Observation Order

Residual

Fig. 2 Residual plot for wear rate

Main Effects Plot for Wear RATE Fitted Means Time

Temp

Particle 1.2

Mean of Wear RATE

1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0

63

105

Fig. 3 Main effects plot for wear rate

25

75

150

120

360

600

80

Optimization of Wear Behavior of Aluminum–Boron Carbide Composites …

45

matrix and thereby allowing maximum area exposure of matrix alloy to the sliding surface. Hence there will be increase in wear rate with the increase in particle size. Similarly, the wear rate of the composite at testing temperature of 25 °C is around 0.3, which is lower than the wear rate of the composite at testing temperature of 75 °C which is 0.95 and is 1.1 at 150 °C. From the graph it is clear that, for the given weight fraction, higher wear rate was found in the composites with higher particle size of reinforcement at both 75 and 150 °C. The wear rate was high at 150 °C than at 75 °C. With the increase in temperature the hardness decreases and the material becomes soft. As the hardness decreases, obviously there will be an increase in the wear rate. On the other hand, as the testing time increases the wear rate decreases. At testing time of 600 s, the wear rate is around 0.6, which is much lower than the wear rate at a testing time of 120 s with 1.1 and 360 s with 0.75. This is due to the fact that initially as the pin comes in contact with the rotating disc very high friction will be generated between the surface of the pin and the disc which leads to the more amount of material removal and higher wear rate and as the time increases the friction gradually decreases due to the effect of plastic deformation of the material. The removed wear debris acts as a protective layer between the surface of the pin and the disc.

4.3 Interaction Plot for Wear Rate The graph in Fig. 4 presents the interaction between the particle size, temperature and time. The interaction between the particle size and the temperature shows that with the increase in the particle size and the testing temperature the wear rate increases and at some point they are almost intersecting around 0.85, which strongly affects the wear rate. The interaction between the particle size and the time shows that it has no effect on wear rate as there is no intersection of variables and the lines are almost parallel to each other. However, at 120 s the wear rate increases due to sudden friction obtained during the interaction of pin and disc. The interaction between the temperature and the time shows that with the increase in the temperature and the time the wear rate increases, and at some point of 25 °C they are almost intersecting around 0.3 which strongly affects the wear rate.

5 Conclusion The present research work on the studies of optimization of wear behavior of aluminum 6061–boron carbide composites using factorial analysis has led to the following conclusions:

46

S. Sanman et al.

Interaction Plot for Wear RATE Fitted Means Particle * Temp

1.6

Temp 25 75 150

Mean of Wear RATE

1.2 0.8 0.4 0.0

Temp * Time

Particle * Time

1.6

Time 120 360 600

1.2 0.8 0.4 0.0 0

63

Particle

105

25

75

150

Temp

Fig. 4 Interaction plot for wear rate

• Stir casting method was successfully employed for preparing of Al-B4 C composites by varying the particle size of the reinforcement with constant 8 wt%. • Wear rate increases with the increase in particle size for a given weight fraction of B4 C. • Wear rate increases with the increase in testing temperature. • Wear rate decreases with the increase in the testing time. From the above analysis, it is identified that the factors, particle size and the testing temperature, significantly affect the wear rate of the composite.

Reference 1. S. Sanman, K.V. Sreenivas Rao, Effect of angle of impingement on air jet erosion wear behavior of chill cast Aluminum-Boron Carbide composites. Mater. Today Proc. 5(10), 21107–21110 (2018) 2. S. Sanman, K. V. Sreenivas Rao, Effect of sand concentration on erosive–corrosive wear behavior of chill cast aluminum–boron carbide composites. Mater. Today Proc. 5(1), 2951–2954 (2018) 3. K.V. Sreenivas Rao, S. Sanman, Comparative evaluation of high temperature wear behavior of chill cast Al-B4 C Composite. Mater. Today Proc. 4(9), 9607–9611 (2017) 4. S. Sanman, K. V. Sreenivas Rao, Effect of temperature on the wear behaviour of Aluminum Boron Carbide composites. Int. J. Appl. Eng. Res. 10(55), 4013–4015 (2015) 5. K.V. Sreenivas Rao, S. Sanman, Analysis of cooling curves, microstructure and properties of chill cast Al-B4 C composites. Adv. Mater. Res. 1101. Trans Tech Publications Ltd (2015)

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6. M. Nagaral, V. Auradi, S.A. Kori, Dry sliding wear behavior of graphite particulate reinforced Al6061 alloy composite materials. Appl. Mech. Mater. 592. Trans Tech Publications Ltd (2014) 7. S. Sanman, K. V. Sreenivas Rao, Influence of reinforcement particulate size and weight fraction on the wear properties of chill cast Al-B4 C composites. Int. J. Appl. Eng. Res. 10(11), 10292– 10295 (2015) 8. K.S. Sridhar Raja, V.K. Bupesh Raja, Effect of boron carbide particle in wear characteristic of cast aluminium A356 composite. IOSR J. Mech. Civil Eng. 73–77 (2014) 9. S. Rajesh, S. Rajakarunakaran, R. Sudhakaran pandiyan, Modeling and optimization of sliding specific wear and coefficient of friction of Aluminum based red-mud matrix composite using Taguchi method and response surface methodology. Mater. Phys. Mech. 15, 150–166 (2012). 10. R. Ranjith Kumar, Velmurugan, Optimization of tribological properties in molybdenum di sulphide and titanium carbide reinforced aluminum composite. IOSR J. Mech. Civil Eng. 47–54 (2014) 11. K.P. Prashanth, G.N. Lokesh, G.P. Prasad, S. Raghavendra, A study on particle weight fraction and extrusion on the mechanical properties and microstructural evaluation of Al-Cu/Fly ash composite. Adv. Mater. Res. 1159, 100–111 (2020)

Effect of Carbon Nanoparticles on the Mechanical Properties of Banana Fiber Reinforced Polymer Navid Bin Mojahid, Kamrujjaman Rubel, Sayed Samiul Newaz, and Nikhil R. Dhar

Abstract Natural fiber-reinforced composites, at present, are having a significant role to meet the challenge of developing materials of high strength to weight ratio for the application in the field of automotive and aerospace. In this research, the effect of carbon nanotube (CNT) as filler material in banana fiber reinforced polymer has been studied by incorporating carbon nanotube and banana fiber with epoxy resin in different weight ratios. The materials were prepared by the hand layup process. Two cases were considered for the study where the sum of weight percentage of fiber and filler in the composite were kept 8% for the first case and 16% for the second one. The experimental results showed remarkable improvement in the tensile and flexural properties of the composite due to the addition of carbon nanotubes. The tensile strength increased by 11.33% for the addition of 1.5% of CNT in the first case and 13.29% for the addition of 2.5% of CNT in the second case. On the other hand, flexural strength increased by 71.5% and 123% respectively for adding CNT of stated weight percentage. Keywords Natural fiber · Composites · Carbon Nanotubes · Hand Layup

1 Introduction At present, composite materials have a wide range of engineering applications in the field of automobile and aerospace industries due to their specialized characteristics. A composite material is a combination of two or more materials that have different physical and chemical properties. When they are combined, it is possible to achieve the combinations of distinct properties of several materials to create unique characteristics. Usually, a composite material has two parts, one is the matrix and the other N. B. Mojahid (B) · K. Rubel · S. S. Newaz Department of Aeronautical Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh e-mail: [email protected] N. R. Dhar Department of Industrial & Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 C. S. Ramesh et al. (eds.), Recent Trends in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-2086-7_4

49

50

N. B. Mojahid et al.

one is fiber. Additionally, fillers or other components may be applied to enhance its properties. The matrix can be categorized into three types, they are polymer, metal and ceramic matrix. The aerospace or automobile industry’s ultimate goal is to develop products that are lighter in weight and overall more efficient than the existing designs. In recent, many research works are being undertaken to meet this goal. Fiber Reinforced Polymer (FRP) materials are having a great role in this sector for developing cost-efficient, high stiffness to weight ratio and high strength to weight ratio materials [1]. Natural fibers are now getting priority in today’s research field in materials science due to their unlimited resources, availability, light weight and low cost [2, 3]. In the present research, the composite material has been developed by the inclusion of banana fiber in a polymer matrix with the addition of carbon nanotubes. Carbon nanotubes are the allotropes of carbon having molecules of cylindrical shape and hexagonal structure. Their diameter can be from 1 to 100 nm [4]. These nanoparticles have motivated research on their various engineering applications and using them as filler materials for composites [5]. These nanoparticles have ultra-high-strength, excellent mechanical and thermal properties. Kumar and Choudhary [6] conducted their research on the mechanical properties of banana and glass fiber reinforced hybrid composites. In their experiment, it has been found that the tensile strength of Hybrid Fiber Reinforced (HFREC) increased by 1.24% than Glass Fiber Reinforced Epoxy Composites. Moharaj and Gangadharan [7] studied the mechanical properties of banana fiber/ glass fiber hybrid composites. They developed a number of plates of different mixing ratios. They found that the plate number four (30(F):70(R)) with reinforcement resin mixture Vajram (Gum Arabic) 80% and Epoxy (20%) possess shows the highest Tensile strength, Flexural strength, Impact strength, and Hardness compare to other five plates. Thomas Mathew et al. [8] determined the tensile strength of 1000 N for only banana fiber reinforced polymer and 1250 for banana fiber and hair reinforced polymer. Adde and Elias [9] investigated the effect of increasing the weight percentage of banana fiber in epoxy resin. The tensile and flexural strength increase with the addition of banana fiber up to 60%. Santhosh et al. [10] had done a comparative analysis of the mechanical properties of Sodium Hydroxide treated and untreated banana fiber-reinforced composite. Salahi et al. [4] worked on the fabrication and characterization of hydroxyapatite-carbon nanotubes composite. They used hydroxyapatite with the matrix component and carbon nanotubes were added with the composites at various weight percentages. They have observed that the bending strength of the material decreased a bit at first, but eventually increases with the increase of carbon nanotubes percentage. Saravanan et al. [11] studied the influence of modified multiwalled carbon nanotubes on mechanical and thermal properties of carbon fiber reinforced epoxy resin hybrid nanocomposites. They experimented by reinforcing the carbon fiber/epoxy resin composite with the different weight percentages of carbon nanotubes that are 0.3, 0.6, 0.9, 1.2 and 1.9%. Their experimental results showed that the tensile strength and flexural strength of the material improves along with the hardness. Ahmadvand, Ali Rohani [12] studied the physical and mechanical properties of polypropylene nanocomposites with the addition of carbon nanotubes (MWCNT).

Effect of Carbon Nanoparticles on the Mechanical Properties of Banana …

51

They experimented with different percentages of carbon nanotubes. For their study, they have concluded that the tensile and flexural strength increased considerably with the addition of CNTs. In this experiment, 8% wt banana fiber was reinforced in epoxy resin in the first case. In the second case, banana fiber loading was kept at 16%. Next 1.5% and 2.5% of carbon nanotubes were incorporated with banana fiber respectively for these two cases. The fibers were treated with 1% Sodium Hydroxide solution in order to remove moisture content, wax and impurities [13]. Chopped fibers of 15 mm to 20 mm length were used [14]. The composite samples were made by hand layup process. Tensile tests and flexural tests have been carried out for each sample and their comparative analysis has been presented.

2 Methodology and Approach In this experiment, banana fiber and carbon nanotubes were used as the reinforcing material and epoxy resin AW 106 and Hardener HV953 have been used as the matrix [7]. The composites were prepared by hand layup process. There were some steps to extract the fiber manually like as- (a) selected the tree from which the fibers were to be extracted, was at the stage just before flowering; (b) several layers of its skin had been peeled off from the stem; (c) cut the layers of the stem into 1.5 feet pieces; (d) the layers of thick skins were then pressed with a heavy roller. This process was continued for a couple of times to make the peels squeezed properly; (e) the squeezed layers were kept in the water for 3 days. It had removed the dust, oil, wax, etc., from the peels and it helped to soften the peels as it starts to rot; (f) when the peels had started to rot, they were taken out of the water and pressed once again to eject the absorbed water inside it. Next, with a sharp blade, the surfaces of the peels were rubbed thoroughly. This process was continued unless the fibers are fully extracted; (g) once the fibers were completely extracted from the peels, they were washed thoroughly in water. While washing in water the fibers got more separated; (h) then the fibers were dried in the sun for a couple of hours. The extracted banana fibers were treated with 1% Sodium Hydroxide solution. Then they were washed thoroughly in water. Next, they were dried for 2 h in sun. This had completely removed the remaining oil, wax and other impurities from the fibers. Sodium Hydroxide was collected from the chemistry laboratory of MIST. A total of five types of samples were prepared at different weight ratios of fiber and fillers. Five Samples of each type were fabricated for tensile and flexural tests. The fibers and fillers portion were kept 8 and 16% in total.

52

N. B. Mojahid et al.

Fig. 1 Banana fibers

2.1 Preparation of Specimen Five mold cavities were prepared for five types of specimen. Wax was used for preventing the adhesiveness of the matrix to the surface of the mold cavity. The resin and hardener were mixed thoroughly in an appropriate ratio. The fibers were chopped into 15–20 mm pieces and arranged at random orientation in the matrix. The molds were pressed evenly with a wooden block to eliminate all the air bubbles created inside. These composites had been cured for 72 h. The specimen sizes were kept according to the ASTM D-638–02 for tensile testing and ASTM D-790 for Flexural Test. To obtain the appropriate dimension, a grinding machine was used (Figs. 1 and 2). Specimen 1, Specimen2, Specimen 3, Specimen 4 and Specimen 5 are referred as S1, S2, S3, S4 and S5, respectively, in this experiment (Fig. 3, Table 1).

2.2 Experimental Approach The tensile test had been carried out according to the ASTM D-638–02. The test was done with the aid of the Universal Testing Machine ‘Hounsfield’. The gage length was kept 50 mm and grip to grip distance was approximately 115 mm. The speed of testing was 5 mm/min. It is the slope of the initial straight-line portion of the stress–Strain curve. It can be calculated from the following equation, E=

σL δ

Here, δ = Deflectionatthe initial portion of curve.

(1)

Effect of Carbon Nanoparticles on the Mechanical Properties of Banana …

53

Fig. 2 a Weighting of Epoxy Resin, b weighting of hardener, c Mixing of resin and hardener, d Arrangement of fibers, e Mixing of CNT, f Placing in mold cavities

Fig. 3 Specimen for tensile (a) and Flexural test (b) Table 1 Composition of the specimens

Specimen no

CNT

Banana fibre

Epoxy Resin + Hardener

(S1)

5%

0%

95%

(S2)

0

8%

92%

(S3)

1.5%

6.5%

92%

(S4)

0

16%

84%

(S5)

2.5%

13.5%

84%

54

N. B. Mojahid et al.

Fig. 4 Setup for tensile test

δ = Corresponding stress. L = Gage length. E = Young’s Modulus. The flexural test had been conducted according to the ASTM Standard D-790. Here the span was taken 80 mm. The test speed has been set to 2 mm/min (Figs. 4 and 5). The tangent modulus of elasticity, also known as the “modulus of elasticity,” is the ratio that is within the elastic limit, of stress to corresponding strain. It is calculated by drawing a tangent to the steepest initial straight-line portion of the load–deflection curve and using the following equation, E=

L 3m Abd 3

(2)

Here, L = Span of the test Specimen (mm), m = Slope of the initial straight-line portion of load–deflection curve, b = width, mm d = thickness, mm.

Effect of Carbon Nanoparticles on the Mechanical Properties of Banana …

55

Fig. 5 Setup for flexural test

3 Results and Discussion 3.1 Tensile Test From tensile testing tensile strength, young’s modulus and maximum rate of strain were obtained. Young’s modulus was calculated from equation (i) (Fig. 6, Table 2).

Fig. 6 Stress versus Deflection Curve for tensile test

56 Table 2 Tensile Test Findings

N. B. Mojahid et al. Specimen

Tensile Strength (MPa)

Young’s Modulus (MPa)

Maximum Strain (%)

1

22

349.65

5.1

2

11.82

300.48

3.92

3

13.16

320

8.10

4

15.12

384.62

4.62

5

17.13

356

4.46

Fig. 7 Stress versus Deflection Curve for flexural test

3.2 Flexural Test Result From flexural testing, Flexural strength, Flexural modulus of Elasticity and maximum rate of strain were obtained. Flexural modulus of Elasticity was calculated from equation (ii) (Fig. 7, Table 3).

3.3 Comparative Analysis of Tensile Properties Results showed that, among the composites containing banana fiber, Specimen 5 gives the maximum strength (Figs. 8, 9 and 10). Young’s modulus increased with the addition of CNT with 8% fiber loading but decreased when added with 16% fiber loading.

Effect of Carbon Nanoparticles on the Mechanical Properties of Banana … Table 3 Findings of Flexural Test Specimen

Flexural Strength(MPa)

Flexural Modulus of Elasticity(GPa)

Maximum Strain (%)

1

101.32

6.684

2.306

2

30.56

1.377

1.627

3

52.43

3.672

1.726

4

42.56

1.5208

2.869

5

95.32

5.5528

6.41

Fig. 8 Tensile strength comparison

Fig. 9 Young modulus comparison

57

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N. B. Mojahid et al.

Fig. 10 Maximum strain comparison

Maximum Strain Rate 8.1

9 8 Strain(%)

7 6

5.1

5

3.92

4

4.62

4.46

S4

S5

3 2 1 0

S1

S2

S3 Samples

Fig. 11 Flexural strength comparison

Maximum Stress(MPa)

120 100

Flexural Strength 101.32

95.32

80

52.43

60

42.56

30.56

40 20 0

S1

S2

S3

S4

S5

Samples

3.4 Comparative Analysis of Flexural Properties From experimental analysis, it was found that, among the composites containing banana fiber, Specimen 5 gave both the maximum strength and Flexural Modulus of elasticity which was consisted of 13.5% banana fiber and 2.5% carbon nanotube (Figs. 11, 12 and 13).

4 Conclusions In this research, the influence of reinforcing carbon nanotube with banana fiber/epoxy composite at two different weight ratios has been investigated. In both weight ratios, the addition of carbon nanotube improved the mechanical properties of banana fiber/epoxy composite. The results showed that Sample 5 gave the most optimum

Modulus of elasticity (GPa)

Effect of Carbon Nanoparticles on the Mechanical Properties of Banana … 8 7

59

Flexural Modulus of Elasticity 6.684 5.5528

6 5

3.672

4 3

1.5208

1.377

2 1 0

S1

S2

S3

S4

S5

Samples

Fig. 12 Flexural modulus of elasticity comparison

Maximum Strain

7

6.41

6 Strain(%)

5 4 3

2.869

2.306

2

1.627

1.726

S2

S3 Samples

1 0

S1

S4

S5

Fig. 13 Flexural strain comparison

result which was the composition of 2.5% carbon nanotube with 13.5% of fiber loading in the epoxy composite. From the comparative analysis of tensile testing, we can see that the increase in the wt. % of carbon nanotube increases the maximum flexural and tensile strength. The comparative analysis of the flexural test indicated that the enhancement of the wt% of carbon nanotube improves the Flexural Modulus of Elasticity. This also initially improves the Young’s Modulus but further addition of CNT resulted in a slight decrease in Young’s Modulus of Elasticity. Without filler, it was observed the composite with 16% fiber loading showed better properties. So, more investigation can be carried out by keeping the fiber loading 16% and varying the ratio of the carbon nanotube. Further satisfying results can be obtained by mixing the nanoparticles by sonication or magnetic stirring so that the fillers will evenly be combined with the matrix and fill the air gaps.

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N. B. Mojahid et al.

In this study, CNTs were used without any surface modification. Surface-modified CNTs may give a much more positive result than unmodified ones. Other mechanical properties like the impact strength, hardness and thermal properties of this developed composite are remained to be studied.

References 1. M.H. Rafiquzzaman, A. Rahman, M. Sayeed, A. Nawazish, M.H. Rahman, Evaluation of mechanical behavior of jute fibre-aluminum powder reinforced hybrid polymer composites. Int. J. Mech. Eng. Autom 3(5), 202–207 (2016) 2. M. Harikrishna, K. Ajeeth, C. Thiagarajan, Fabrication and mechanical properties of hybrid natural fiber composites (Jute/Banana/Glass) 119(15), 685–696 (2018) 3. K. Murali Mohan Rao, K. Mohana Rao, A.V. Ratna Prasad, Fabrication and testing of natural fibre composites: Vakka, sisal, bamboo and banana. Mater. Des. 31(1), 508–513 (2010). https:// doi.org/10.1016/j.matdes.2009.06.023. 4. E. Salahi, A. Rajabi, Fabrication and characterization of hydroxyapatite-carbon nano tubes composites. Am. J. Nanosci. 2(4), 41–45 (2016). https://doi.org/10.11648/j.ajn.20160204.11 5. M.R. Ayatollahi, S. Shadlou, M.M. Shokrieh, M. Chitsazzadeh, Effect of multi-walled carbon nanotube aspect ratio on mechanical and electrical properties of epoxy-based nanocomposites. Polym. Test. 30(5), 548–556 (2011). https://doi.org/10.1016/j.polymertesting.2011.04.008 6. A. Kumar, D. Choudhary, Development of glass/jute fibers reinforced epoxy composite. Int. J. Eng. Res. Appl. 3(6), 1230–1235 (2013) 7. M. Mohanraj, Experimental investigation of mechanical characteristics of natural resin reinforced glass/banana fiber composite, SSRG I. J. Civil Eng. pp. 144–150 (2018) 8. T. Mathew, V.S.K.P. Ak, S. Nr, P. Sh, P. Sridharan, Fabrication & testing of banana-hair reinforced hybrid composite. 7(4), 6667–6670 (2017) 9. K. Alemayehu, A. Yeshurun, L.R. Elias, Experimental evaluation of mechanical properties of banana fiber reinforced polymer composites. 9(6), 724–733 (2018) 10. J. Santhosh, N. Balanarasimman, R. Chandrasekar, S, Raja, Study of properties of banan fiber reinforced composites. Int. J. Res. Eng. Tech. 3 (11) (2014) 11. R. Saravanan, A. Sureshbabu, T. Maridurai, Influence of surface modified mwcnt on mechanical and thermal properties of carbon fiber/epoxy resin hybrid. Digest J. Nanomater. Biostruct. 11(4), 303–1309 (2016) 12. N. Ahmadvand, Improvement of mechanical and physical property of polypropylene nano composites by the addition of multi-walled carbon nano tube. Nanosci. Nanometrol. 3(1), 20 (2017). https://doi.org/10.11648/j.nsnm.20170301.14 13. N. Venkateshwaran, A. Elaya Perumal, D. Arunsundaranayagam, Fiber surface treatment and its effect on mechanical and visco-elastic behaviour of banana/epoxy composite. Mater. Des. 47, 151–159 (2013). https://doi.org/10.1016/j.matdes.2012.12.001. 14. N. Venkateshwaran, A. Elayaperumal, M.S. Jagatheeshwaran, Effect of fiber length and fiber content on mechanical properties of banana fiber/epoxy composite. J. Reinf. Plast. Compos. 30(19), 1621–1627 (2011). https://doi.org/10.1177/0731684411426810

A Study on Tensile and Tear Properties for Chitosan Blended with and Without Natural Fiber Films K. P. Prashanth, S. Sanman, and G. N. Lokesh

Abstract A composite film was prepared by blending chitosan and natural fibers with a different formulation. The results of the incorporation of natural fibers with chitosan on the strength of film blends were investigated. The tensile strength is important to identify and analyse sample for suitable application. Overall, it was found difficult to establish higher chitosan compositions that cause resistance to flow due to the higher viscosity of the more rigid chitosan chain.. The ATR-IR test was carried out to determine the presence of a functional group of chitosan blended films. It was noticed that the composite film has a functional group; they interact as a composite blend. The functional group of exhibited amines, amides and esters is ideal for oxidation processes. The tensile test, conducted on Chitosan, sisal fiber-reinforced Chitosan, banana fiber reinforced Chitosan, and coconut fiber reinforced Chitosan specimens were considered. Tests were conducted utilizing UTM. The mechanical property of specimens was studied according to the three parameters which are tensile strength, elongation at break and Engineering UTS. Tear testing has been a concern in this research work. The experimental tear tests showed different results for both chitosan blended with and without natural fibers materials when they were subjected to load in different material directions. The tear test approach deals with assessing the tear resistance of flexible plastic film and sheeting at very low loading levels. The results and analyses for both tensile and tear tests are dependent on compounding chitosan with and without added natural fibers as reinforcement like sisal, banana and coconut fiber. Higher strength was found with higher chitosan composition of natural fibers. The film based on chitosan can potentially be used for biodegradation, antimicrobial packaging and can also be efficient in food preservation. Keywords Tensile · Tear · ATR-IR · Chitosan · Natural fibers

K. P. Prashanth (B) · S. Sanman Department of Automobile Engineering, Acharya Institute of Technology, Bengaluru, India e-mail: [email protected] G. N. Lokesh School of Engineering, Presidency University, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 C. S. Ramesh et al. (eds.), Recent Trends in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-2086-7_5

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1 Introduction Recently, chitosan, a marine polysaccharide derived from arthropod shells such as crab, shrimp and lobster, has become the most common biopolymer for use in therapeutic procedures. A helpful and exciting bioactive polymer is the deacetylated chitin derivative chitosan. Despite its biodegradability [1], it has many reactive amino side groups that give opportunities for chemical changes, the formation, by the manner of graft reactions and ionic interactions, of a broad spectrum of helpful derivatives accessible on a commercial basis. This study examines modern research into the structure, characteristics, and applications in multiple industrial and biomedical areas in chitin and chitosan [2]. Recently, the field of natural fiber composite (NFC) has seen a fast development in studies and innovation. Because of the benefits of these products relative to others, such as synthetic fiber composites, including low environmental impact and low price, interest is justified and promotes their potential across a broad spectrum of apps. A great deal of effort has been made to improve their mechanical performance to expand the capacities and applications of this category of materials [3, 4].

2 Material Materials used in this research are chitosan and natural fibers like sisal, banana, coir. Polymer composite materials are varied with chitosan composition 2, 2.5, 3 g and 1, 2 g fix the amount of mercerized natural fibers (treated with 5% NaOH). Polymer composite material was prepared by using an open mould casting process.

3 Methodology Samples were formulated as in Table 1 and polymer composite materials were mixed via open mould processing. Chitosan is dissolved with a 2% acetic acid solution. Fibers were treated with 5% NaOH for mercerization [5]. Attenuated total reflectance infrared spectroscopy (ATR-IR) also known as Fourier Transform Infrared (FTIR), infrared spectroscopy preferred approach. An example of IR radiation is gone by infrared spectroscopy. The illustration absorbs a portion of the infrared radiation and some of it goes through (transmitted). The subsequent range speaks to subatomic retention and transmission, thus making the example a special subatomic image. No two extraordinary atomic structures, like a single label, establish a similar infrared spectrum. This makes infrared spectroscopy useful for a few kinds of exams [6, 7]. Ultimate tensile strength (UTS), often abbreviated to tensile strength (TS), ultimate strength, is a material or structure’s ability to resist elongated loads as compared

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Table 1 Composition chart for sample preparation Sample

Chitosan in grams

Natural fiber

Mercerized natural fiber in grams

Sample coding

1

2

2

2.5

–

–

2C

–

–

3

2.5C

3

–

–

3C

4

2

Sisal

1

2C1MS5

5

2.5

1

2.5C1MS5

6

3

2

3C2MS5

7

2

1

2C1MB5

8

2.5

1

2.5C1MB5

9

3

2

3C2MB5

10

2

1

2C1MC5

11

2.5

1

2.5C1MC5

12

3

2

3C2MC5

Banana

Coir

to compressive strength, which can resist loads that tend to decrease size. Tensile tests were carried out according to ASTMD638. Instron Model 3360 Tensile Tester with a load cell or equivalent of 0.2 kN [8]. The testing machine must be equipped with a load cell whose compliance within the range being measured is a maximum of 2% of the specimen expansion [7, 9, 10]. Digital (as opposed to analog) self-calibrating load cells are preferred as they eliminate the need and potential error associated with using external weights to calibrate analog load cells. The testing machine must be equipped with a device to record the tensile load and the quantity of grip separation, both of which should be precise to ±2%. The grip separation rate must be precise to ±0.1% and adjustable from about 0–50 mm/min [11] (Fig. 1). The tear test method includes the determination of flexible plastic film’s tear resistance as sheeting at very small loading rates of 51 mm/min and is designed to evaluate the tearing force. This test method’s sample geometry generates a concentration of stress in a tiny region of the specimen. The maximum stress, generally discovered near the beginning of tearing, is reported in newton’s as the tear resistance. This test method uses a steady separation rate of the test specimen holding the grips. The extension of the specimen can be measured by grip separation in this test method. A steady rate-of- grip separation machine measures the force to initiate tearing across a particular geometry of a film or sheeting specimen [7]. From the load-time or load–displacement data, the force needed to start the tear is calculated. Plastic film or sheeting tear resistance is a complex function of its ultimate rupture resistance. In this test technique, the sample geometry and speed of testing are regulated to generate tearing at rates far below those generally found in service in a tiny region of stress concentration. The data from this test method provide comparative information to rank plastic samples of comparable composition tearing resistance [11] (Fig. 2).

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Fig. 1 Tensile test specimen specifications

Fig. 2 Tear test specimen specifications

4 Result and Discussion 4.1 ATR-IR Spectroscopic Studies ATR-IR Spectroscopic Studies carried out based on the combination of matrix and sisal, banana, coconut fiber materials. The spectra of the region’s pure chitosan specimens ranging from 1200 to 1800 cm−1 , large peaks for study. This is the area where the deformation of carbonyl, C–O–/NHR, amine, NH2 and ammonium, NH3 + band, OH and CH occur. The band at 1380 cm−1 is due to the primary alcoholic groups –CO stretches (CH2 –OH), and reflects the free main amino group (NH2 ) at 1262 cm−1 . Owing to the anti-symmetric stretching of the C–O–C bridge and skeletal movements including C–O–C stretching, the band at 1153 cm−1 and 1082 cm−1 . The 1650 cm−1 band represents an acetylated amino group, which is due to the C=O stretching vibrations of (Amide I) O–C–NHR. NH2 bending vibrations are assigned to the 1590 cm−1 band (amide II).

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Fig. 3 ATR-IR representations for Chitosan and Natural fibers reinforced chitosan

The NH2 bending vibrations (1590 cm−1 ) for natural fibers reinforced chitosan (sisal, banana, and coconut fiber) vanished, and the new NH3 + bending vibration band appeared at 1630 cm−1 and 1568 cm−1 . Such findings indicate that the H+ supplied by acetic acid protonated the NH2 groups inside the chitosan chains. In this work the carbonyl band for the spectrum of pure chitosan (CHP) is observed at 1650 cm−1 , the amine (NH2 ) band at 1590 cm−1 and the deformation band OH and CH at 1420 cm−1 . These shifts in IR peaks indicated that the interfacial interaction through the formation of new bonds between chitosan and fibers stabilized the film. Figure 3 shows the ATR-IR representations for Chitosan and Natural fibers reinforced chitosan. Table 2 shows ATR-IR peak position and description for Chitosan and Natural fibers reinforced chitosan.

4.2 Tensile Test The tensile property of films was studied according to the four parameters which are tensile strength, peak load, displacement and strain%. These results were analyses that depend on chitosan with and without added natural fibers. Figure 4 shows the tensile strength of three samples with various chitosan compositions without natural fibers. For sample, the film showed the highest tensile strength ~10.8 MPa with 3% chitosan. Lower amounts of chitosan 2%, exhibited peak load ~3.9 N. 2% chitosan blend also showed the lowest elongation compared to other samples. Table 3 shows the study according to the four parameters.

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Table 2 ATR-IR peak position and description for Chitosan and Natural fibers reinforced chitosan

Peak position (cm−1 )

Assignment

900

NH wag primary amine

1153

C–O–C vibrations

1262

CH wag (ring)

1323

OH and CH deformation ring

1380

CH symmetrical deformation bend

1420

OH and CH deformation ring

1590

NH2 deformation

1650

Amide I (C=O)

2878

C–H asymmetrical stretching

2913

C–H symmetrical stretching

3317

N–H2 asymmetrical stretching

3369

N–H2 symmetrical stretching

3441

O–H Stretching

Fig. 4 Tensile strength of various chitosan compositions without natural fibers Table 3 Tensile test results for chitosan blended specimens Specimen

Peak load (N)

Displacement (mm)

Tensile strength (Mpa)

Strain %

2C

3.9

4.2

9.8

8.2

2.5C

5.3

5.2

10.3

8.1

3C

5.9

4.9

10.8

9.2

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Fig. 5 Tensile strength of various chitosan compositions with sisal natural fiber

Figure 5 shows the tensile strength of three samples with various chitosan compositions with sisal fibers. For sample, 3C2MS5 film showed the highest tensile strength ~16.96 MPa with 3% chitosan and 2% mersidized sisal fiber. Lower amounts of chitosan 2%, exhibited peak load ~4 N. 2% chitosan blend also showed the lowest elongation compared to other samples. Table 4 shows a study according to the four parameters of sisal fiber reinforced with chitosan blended films for various compositions. Figure 6 shows the tensile strength of three samples with various chitosan compositions with banana fibers. For sample 2.5C1MB5 film showed the highest tensile strength ~17.8 MPa with 2.5% chitosan and 1% mercerized banana fiber. Lower amounts of chitosan 2%, exhibited peak load ~1.46 N. 2% chitosan blend also showed the lowest elongation compared to other samples. Table 5 shows a study according to the four parameters of banana fiber reinforced with chitosan blended films for various compositions. Figure 7 shows the tensile strength of three samples with various chitosan compositions with coir fibers. For sample, 3C2MC5 film showed the highest tensile strength ~22.5 MPa with 3% chitosan and 2% mercerized coir fiber. Lower amounts of chitosan 2.5%, exhibited peak load ~6.73 N. 2% chitosan blend also showed the lowest elongation compared to other samples. Table 6 shows a study according to the four parameters of coir fiber reinforced with chitosan blended films for various compositions. Table 4 Tensile test results for chitosan blended sisal specimens Specimen

Peak load (N)

Displacement (mm)

Tensile strength (Mpa)

Strain %

2C1MS5

4

3.2

12.2

8.5

2.5C1MS5

7.1

8.1

14.5

9.2

3C2MS5

7.4

5.5

16.96

9.7

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Fig. 6 Tensile strength of various chitosan compositions with banana natural fiber Table 5 Tensile test results for chitosan blended banana specimens Specimen

Peak load (N)

Displacement (mm)

Tensile strength (Mpa)

Strain %

2C2MB5

1.46

4.5

16.1

8.2

2.5C1MB5

8.6

6.4

17.8

9.5

3C2MB5

8.6

8.8

15.5

9.6

Fig. 7 Tensile strength of various chitosan compositions with coir natural fiber

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Table 6 Tensile test results for chitosan blended coir specimens Specimen

Peak load (N)

Displacement (mm)

Tensile strength (Mpa)

Strain %

2C1MC5

9

6.8

18.3

9.2

2.5C1MC5

6.73

8.2

14.8

9.8

3C2MC5

8.6

8.2

22.5

9.89

Chitosan does not give any enhancement to film strength for blends. Natural fibers could possibly plasticize and cause better interaction between the chitosan and natural fibers; hence increasing the strength with greater compatibility between natural fibers and chitosan for all composition.

4.3 Tear Test The tear property of films was studied according to the four parameters which are maximum tear force, displacement, stress and strain%. These results were analyses that depend on chitosan with and without added natural fibers. Figure 8 shows the maximum tear force of three samples with various chitosan compositions without natural fibers. For sample film showed the highest tear force ~20.06 N with 2.5% chitosan. Chitosan with 2.5% exhibited the lowest elongation of 0.922 mm, maximum stress of 5.36 N/mm2 and maximum strain of 2.25% compared to other samples. Table 7 shows the study according to the four parameters.

Fig. 8 Tear strength of various chitosan compositions without natural fiber

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Table 7 Tear test results for chitosan specimens Specimen

Max force (N)

Max displacement (mm)

Max Stress (N/mm2 )

Max strain (%)

2C

16.784

0.65699

3.4805

0.8945

2.5C

20.0645

0.92245

5.3661

2.2531

3C

17.65624

0.839

3.7

1.67

Fig. 9 Tear strength of various chitosan compositions with sisal natural fiber

Figure 9 shows the maximum tear force of three samples with various chitosan compositions with natural fibers. For 2.5C1MS5 sample film showed the highest tear force ~40.68 N with 2.5% chitosan. Chitosan with 2.5% exhibited the highest elongation of 2.52 mm, maximum stress of 2.644 N/mm2 and maximum strain of 3.7% compared to other samples. Table 8 shows the study according to the four parameters. Figure 10 shows the maximum tear force of three samples with various chitosan compositions with natural fibers. For 2.5C1MB5 sample film showed the highest tear force ~19.06 N with 2.5% chitosan. Chitosan with 2.5% exhibited the highest elongation of 0.763 mm, maximum stress of 0.928 N/mm2 and maximum strain of Table 8 Tear test results for chitosan blended sisal specimens Specimen

Max force (N) Max displacement (mm) Max stress (N/mm2 ) Max strain (%)

2C1MS5

37.618

0.8334

0.92547

2.10962

2.5C1MS5 40.6857

2.5276

2.64447

3.7006

3C2MS5

1.1895

1.26

2.379

38.4063

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Fig. 10 Tear strength of various chitosan compositions with banana natural fiber

Table 9 Tear test results for chitosan blended banana specimens Specimen

Max force (N)

Max displacement (mm)

Max stress (N/mm2)

Max strain (%)

2C2MB5

16.0274

0.34559

0.582893

0.6406

2.5C1MB5

19.0658

0.763285

0.928445

0.56077

3C2MB5

17.1953

0.4565

0.72

0.913

0.56% compared to other samples. Table 9 shows the study according to the four parameters. Figure 11 shows the maximum tear force of three samples with various chitosan compositions with natural fibers. For 2.5C1MC5 sample film showed the highest tear force ~32.64 N with 2.5% chitosan. Chitosan with 2.5% exhibited the highest elongation of 1.344 mm, maximum stress of 2.91 N/mm2 and maximum strain of 3.04% compared to other samples. Table 10 shows the study according to the four parameters. Chitosan with 2.5% gives enhancement to film strength for blends. Natural fibers could possibly plasticize and cause better interaction between the chitosan and natural fibers. For the tear test, it is observed that an increase in the percentage of chitosan and natural fibers leads to brittle material for the results.

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Fig. 11 Tear strength of various chitosan compositions with coir natural fiber

Table 10 Tear test results for chitosan blended coir specimens Specimen

Max force (N)

Max displacement (mm)

Max stress (N/mm2 )

Max strain (%)

2C1MC5

29.81845

0.7120

0.84879

1.0903

2.5C1MC5

32.649

1.34452

2.91398

3.04575

3C2MC5

31.0727

0.70929

1.54961

1.454

5 Conclusion In conclusion, the comparison between the different percentages of chitosan in the composite film by adding natural fibers and without natural fibers was evaluated. The evaluation is based on tensile testing; tear testing and Attenuated total reflectance infrared spectroscopy. The tensile test shows the sample with 2% Chitosan gave the low tensile strength, lower elongation at break of the film and the higher stiffness were considerably improved by adding the natural fiber to the film-forming solution. By adding the natural fibers in chitosan films were improved the tensile characteristics of polymer composite materials. The tear test shows the sample with 2.5% chitosan gave the high tear strength of the film and the strength was considerably resulted low by the addition of increased chitosan and natural fiber to the film-forming solution which maybe because brittle in nature. The chitosan-based film can be potentially used as biodegradation, antimicrobial packaging and also can be effective in food preservation. The natural fibers produce the packaging become stronger and less

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elastic than without adding the Natural fibers [12]. The ATR-IR shows the functional group and characteristics between the chitosan with and without natural fibers.

Reference 1. K.P. Prashanth, H.G. Hanumantharaju, G.N. Lokesh, Synthesis and characterization study of chitosan based natural fiber: biodegradable polymer composite, in AIP Conference Proceedings 2057 (2019) 2. K.P. Prashanth, Dr. H.G. Hanumantharaju, Characterization and analysis of polymers used as artificial skin. Mater. Today: Proc. 5(1), 2488–2495 (2018) 3. K.P. Prashanth, Dr. H.G. Hanumantharaju, Preparation and characterization studyof biodegradable polymer composite. JETIR 5(9) (2018). ISSN-2349–5162 4. P.K. Dutta, J. Datta Chitin, Chitosan: Chemistry, properties and application, Department of Chemistry, NIT, Allahabad, January 2004 5. K.P. Prashanth, Dr. H.G. Hanumantharaju, Dr. J. Aravinda, Development and characterization study of Chitosan-Sisal fiber thin films. Int. J. Res. Anal. Rev. (IJRAR) 5(3) (2018). E-ISSN 2348–1269, P- ISSN 2349–5138 6. Dr. H.G. Hanumantharaju, K.P. Prashanth, Mechanical and structural investigation of Chitosan as biodegradable polymer. Int. J. Eng. Sci. Res. (IJESR), Special Issue, Article no 26 (2019). ISSN 2277–2685, 7. K.P. Prashanth, H.G. Hanumantharaju, R. Bakshi, Bio-Materials Science and Engineering (LAMBERT Academic Publishing, Chap.1, pp. 07–25, March 2020) 8. H.G. Hanumantharaju, H.K. Shivanand, K.P. Prashanth, K. Suresh Kumar, S.P. Jagadish, Study on hydroxyapatite coating on biomaterials by plasma spray method. Int. J. Eng. Sci. Technol. 4(9) (2012), ISSN 4152–4159 9. K.N. Sandeep, R. Shadakshari, K.P. Prashanth, Mechanical and barrier properties of biodegradable films made from Chitosan and natural fiber blends. Int. J. Sci. Res. Rev. 7, (5) (2019). ISSN: 2279–0543 10. Y.-W. Cheng, D.T. Read, J.D. McColskey, J.E. Wright, A tensile-testing technique for micrometer-sized free-standing thin films. Thin Solid Films 484, 426–432 (2005) 11. P.N. Sudha, A. Soundararajan, Bio-na nocomposites of Chitosan for Multitissue Engineering Applications, 10.1201/b15636-28, 8 October 2007 12. K.P. Prashanth, H.G. Dr. Hanumantharaju, Preparation and characterization study of ChitosanBanana fiber polymer composite for packaging and tissue designing. Manuf. Technol. Today (MTT), (ISSN: 0972–7396), 17(09) (2018)

Effect of Cryogenic Treatment on Mechanical Properties of Al–SiC Composites S. Raghavendra, N. Satish, and B. S. Ajay Kumar

Abstract The machining process of cryogenic treated composite materials is a challenging task and the process of machining these materials is treated to be an important technique among the manufacturing process. The present study involves the processing of Al6061-SiC composite material by stir casting process, wherein preheated SiC particles are added as reinforcement. The primary processed composites are subjected to cryogenic treatment and the mechanical properties, thermal properties are evaluated to study the influence of cryogenic treatment. The tests include the Hardness test, Impact test and Thermal Conductivity test. Results of the present investigation include improved mechanical properties in terms of hardness, improved thermal properties due to cryogenic treatment. Keywords Composites · Cryogenic treatment · MMC · Al–SiC · Tensile strength · Wear study

1 Introduction Present applications of the aerospace or the automobile parts demand the materials with high strength to weight ratio, One class of such materials that replaces the traditional materials are the composite materials. Matrix and reinforcement are the two main categories of constituent materials. The matrix holds the reinforcement to form the desired shape while the reinforcement improves the overall mechanical properties of the matrix. When designed properly, composite materials exhibit better strength and toughness when compared with monolithic materials. Reinforcement material Silicon carbide (SiC) is added to the matrix material to enhance the properties of the materials. S. Raghavendra (B) · N. Satish Bangalore Institute of Technology, Mechanical Engineering Department, Bangalore, India B. S. A. Kumar KSIT, Mechanical Engineering Department, Bangalore, India e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 C. S. Ramesh et al. (eds.), Recent Trends in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-2086-7_6

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Fig. 1 Stir casting schematic representation

Figure 1 shows the schematic representation of the stir casting process employed in the present study. The preheated reinforcement particles are added to the molten material and mixed well to obtain uniform distribution.

2 Literature Review Bodunrin et al. [1] studied on aluminium hybrid composites consisting of more than one type of reinforcement. These materials are of keen interest that play a vital role in the present scenario. The different combinations of reinforcing materials and their effects on the mechanical, corrosion and tribological behaviours have been studied. The presence of additional material into the matrix material and its processing parameters determines the performance. Sijo et al. [2] studied Aluminium reinforced with varying percentages of B4C (2.5, 5 and 7.5%) by primary processing. The result indicates the amount of the B4C enhances the density of the composites that leads to an increase in the hardness of the composite material. Results indicate that the compressive strength of the composites increased with an increase in the weight percentage of the boron carbide. Balasivanandha et al. [3] investigated the influence of varying stirring speed and stirring time on the properties of the processed composite material with aluminium alloy—silicon carbide with 10% SiC. The results obtained indicate the a lower stirring speed with lower stirring time leads to the formation of particle group or the agglomerates and an increase in stirring time and speed resulted in better distribution of particles.

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Tamer et al. [4] studied Aluminium material reinforced with silicon carbide particles followed by evaluation of mechanical properties. Results obtained indicated that an increase in reinforcement percentage leads to an increase in overall mechanical properties with reduced impact toughness. Srivatsan and Umahsankhar [5] studied the fracture behaviour of SiC reinforced MMCs and the results indicate that temperature plays a predominant role in determining the strength of the materials. The microstructure of the composite materials depends on the aging time and the temperature which leads to changes in the mechanical and thermal properties of the materials. Natarajan and Madevha [6] studied the tribological properties of composite materials reinforced with SiC and compared it against grey cast iron. The tribological studies indicate that the properties of the composite materials are better than the grey cast iron materials. The presence of reinforcement materials helps in improving the tribological properties of the materials.

3 Experimental Work Experimental work involves stir casting of the Al6061 with SiC. Figure 2 indicates the Furnace and Fig. 3 shows the SiC Reinforcement, Aluminium Silicon carbide composite is prepared in four different compositions such as Al with SiC wt% of 2, 4, 6 and 8. Figure 4 shows the cryogenic unit with a nitrogen cooling facility in the range of −185 °C (−301 °F). Fig. 2 Stir casting furnace

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Fig. 3 SiC reinforcement particles

Fig. 4 Cryogenic treatment unit

4 Results and Discussion 4.1 Brinell Hardness Test The BHN of with and without reinforcement are carried out with 5 mm hardened steel ball indenter with 100 kg load for 30 s. Tool makers microscope measures

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Fig. 5 Brinell hardness number

Table 1 BHN versus weight percentage

Sl. No.

% of reinforcement

Untreated

Cryogenic treated

1

2

54

62

2

4

58

60

3

6

61

64

4

8

63

66

the indentation diameter. Figure 5 indicates that the cryogenically treated material exhibits higher strength than the untreated composites. Improved is strength is due to the improvement in the grain structure of the composite materials due to the cryogenic treatment (Table 1).

4.2 Impact Test Table 2 and Fig. 6 shows the impact energy of the composite materials, it can be observed that the cryogenically treated composite exhibits higher impact energy due to improvement in the hardness of the material. Table 2 Impact strength versus weight percentage

Sl. No.

% of reinforcement

Untreated

Cryogenic treated

1

2

6

6

2

4

4

5

3

6

3

4

4

8

3

4

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Fig. 6 Impact strength

Fig. 7 Thermal conductivity

4.3 Thermal Conductivity The thermal conductivity of a material is a measure of its ability to conduct heat. Increase in SiC, the thermal conductivity sharply decreased due to the existence of porosity. It suggests that the presence of pores reduces thermal conductivity (Fig. 7 and Table 3).

Effect of Cryogenic Treatment on Mechanical Properties … Table 3 Thermal conductivity versus weight percentage

81

Sl. No.

% of reinforcement

Untreated

Cryogenic treated

1

2

122.5

121

4

123.1

122.3

3

6

125.1

121

4

8

125.1

121

5 Conclusions Present work provides the following conclusions • Stir casting technique was successfully adopted in the processing of Al6061 SiC alloy containing 2, 4, 6 and 8% of SiC powders as reinforcement. • The hardness of the composites is found to increase with the increase in the reinforcement of SiC and the higher hardness noticed for the 8% of SiC powder addition. • The hardness of cryogenically treated composite showed improved hardness when compared to untreated conditions. • Impact energy has been found to be increased with the addition of SiC and composite with 8% SiC showed the highest energy absorption capacity compared to others. • Thermal conductivity and the Impact energy of cryogenically treated composites are found to be decreased after the cryogenic treatment, the reductions might be due to the ductility of the composites.

References 1. M.O. Bodunrin, K.K. Alaneme, L.H. Chown, Aluminium matrix hybrid composites: a review of reinforcement philosophies; mechanical, corrosion and tribological characteristics. J. Mater. Res. Technol. 4(4), 434–445 (October-December 2015,) 2. M.T. Sijo, K.R. Jayadevan, Analysis of stir cast aluminium B4C metal matrix composite: a comprehensive review, in International Conference on Emerging Trends in Engineering Science and Technology (ICETEST-15) (Elsevier Publication, Procedia Technology, 2016), pp. 379–385 3. P. Balasivandha, K. Madhu, Experimental studies on mechanical characteristics of hybrid aluminium metal matrix composite and SiC, in Machining Technology, Machine Tools and Operations, ed. by H.A. Youssef, H. El-Hofy (CRC Press, 2008), pp. 371–390, Print 4. O. Tamer, K.B. Hhan, A study of the effect of reinforcement particles on diffusion bonding al metal–matrix composite (MMC). Minerals Metals Mater. Soc. ASM Int. 43B, 627–634 5. A. Srinivasan, L. Umahsankhar, Tensile properties and strengthening effects of SiC reinforces particles. J. Alloy. Compd. 72, 8388–8406 (2018) 6. A. Natarajan, N. Madevha, Influence of reinforcement contents on tribological properties of Al composites applicable to brake drum. Ceram. Int. 247, 8842–8860 (2018)

Influence of Various % of Carbon Nanotubes Reinforced AZ91 Magnesium Alloy Nano Composite G. M. Sandeep, Bopanna Satish Babu, N. Vinayaka, Muralidhar, and S. L. Vijay Kumar

Abstract Carbon Nanotube (CNT)-reinforced AZ91 metal matrix composites (MMC) is an advanced alloy used in designing lightweight vehicles and aircrafts. Powder metallurgy (ball milling), cold pressing and heat treatment prepare the composites used in this paper. It is observed, that CNTs increase mechanical properties. The addition of CNTs improves the yield strength and ultimate tensile strength. The coefficient frictions vary with the amount of CNTs added into the AZ91. The wear properties are found to decreases with an increase in CNT due to its selflubrication property. However, the marginal increase in CNTs leads to reduce some wear aspects, which are discussed in this paper. Index Term First term · Second term · Third term · Fourth term · Fifth term · Sixth term

1 Introduction Magnesium alloys are less dense material (density –1760 to 1870 kg/m3 ) are slowly gaining usage in aircraft and lightweight automobiles, not only due to its less weight but also due to good manufacturability and damping ability [1] Due to its hexagonal closed packed structure, it has less ductility. AZ91 is a very popular magnesium alloy, which is used in lightweight construction flat parts such as ribs and brackets. The mechanical properties of magnesium alloy can be enhanced by adding various other constitutes like carbon, CNT, titanium carbide and ceramic [2–6]. Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made. G. M. Sandeep (B) · B. Satish Babu · Muralidhar · S. L. Vijay Kumar Presidency University, Bengaluru, India e-mail: [email protected] N. Vinayaka Nitte Meenakshi Institute of Technology, Bengaluru, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 C. S. Ramesh et al. (eds.), Recent Trends in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-2086-7_7

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Fig. 1 Block diagram of methodology

Ball milling

Cold compacng

Heat treatment

Wear and Hardness Test

Carbon nanotubes (CNT) are the type of nanomaterials that comprise a twodimensional hexagonal lattice of carbon atoms bent and merged in one direction to form a hollow cylinder. Multi-walled nanotubes (MWNTs) comprise multiple rolled layers of graphene. Over recent times, CNT-reinforced Mg matrix composites have established much thoughtfulness, and quite a few methods have been proposed to fabricate these composite such as casting, powder metallurgy (PM) with ball milling, stir casting with ultrasonic vibration and many more. Numerous earlier works have shown that the PM method can integrate more CNTs into the Mg matrix and achieve a uniform distribution. Hence, for this work PM method with ball milling has been used and reported.

2 Objectives The objectives of the present work are to prepare Carbon Nanotube reinforced AZ91 (CNT-AZ91) composites using powder metallurgy technique and ball milling, to evaluate the influence of heat treatment on the CNT-AZ91 composites, evaluation of various hardness of the prepared CNT-AZ91 composites and to evaluate the of wear behaviour of the composites using pin on disc wear and friction monitor (Fig. 1).

3 Ball Milling A grind and blending device was used to mix CNTs and AZ91 powders. The machine used was a planetary ball mill with stainless steel balls and the inside of the cylinder was lined with manganese steel. The high centrifugal forces of a planetary ball mill resulted in very high crushing and therefore diminutive grinding intervals. A.

Cold compacting and heat treatment

The cold compaction was done through Universal Testing Machine (UTM) through die as shown in Fig. 2. Cold compaction results in the providing a solid shape for the part, Increases the density, Properties of the green compact depend on the applied stress levels and precise control procedures. Porosities typically start high, and can be reduced to values such that the density of the green compact comes close to that of the bulk materials. Heat treatment was done using a forge furnace for 100 °C for 30 min post obtaining the green compact.

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Fig. 2 Cold compaction process

B.

Wear test

Friction is the main factor causing weakening of materials used in everyday life components. Abrasion and wear tests will give us data to compare materials or coatings and can help predict the life of a material or coating [4, 7]. An apparatus for wear testing is termed as wear tester; many different wear testing arrangements are used in laboratories around the world. However big difference of one arrangement as compared with another, a wear tester used in this study is a DUCOM make pin-on-disc wear testing machine as specified by ASTM G-17b standards. Either the pin or disk can be used as the test specimen material. For the current study, we have used the pin as composite to rub against a tungsten carbide disc [5, 8, 9].

4 Experiment After taking the weights according to the theoretically calculated percentages as shown in Tables 1 and 2. The weighing will be done using weigh balance. We used an electric weigh balance Shimadzu ATX224. It has a maximum weighing capacity of 0.22 kg having an accuracy of 0.1 mg and readings will be shown in a digital meter. After weighing the samples, mixing the powders for calculated percentages as mentioned in the Table 1 will be done [3, 10].

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Table 1 Weights and % of CNTs and AZ91 Sl. No.

% of AZ91

% of CNTs

Amount of AZ91 (gms)

Amount of CNTs in (gms)

1

99.5

0.5

99.5

0.5

2

99

1

99

1

3

98.5

1.5

98.5

1.5

Table 2 Harness test results Sl. No.

AZ91-CNTs % specimen (%)

Trial 1

Trial 2

Trial 3

Average

1

0.5

43

45

41

43

2

1

49

51

49

49.66

3

1.5

65

63

60

62.66

Fig. 3 Green compact

The powder so weighted were introduced to the planetary ball milling machine and run for a duration of 60 min. The load was varied from 70 to 125 kN initially for the first sample, which had only AZ91 powder, the best results came out for the load between 105 and 125 kN. The specimen to which load applied was below 105 kN were not successfully compacted resulting in powder formation and loads above 125 kN created cracks in the prepared samples. Hence, we chose to keep the load of UTM for 115 kN for the rest of the samples. The resulting green compact is shown in Fig. 3. A Rockwell hardness test and pin on disc wear test were conducted post obtaining the heat-treated specimen.

5 Results Hardness a fundamental material property. It is defined as the resistance to indentation, and it is a destructive test conducted by measuring the depth of the indentation. The Rockwell test is generally preferred as it is more accurate than other hardness

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testing methods. The Rockwell test method is used on all metals, except in condition where the surface conditions would introduce too many disparities, where the material is too soft. The result obtained by the hardness is reported in Table 2. The results show an increase in hardness as the % of CNT is increased as shown in Table 2. In a Pin on disc wear tester, a pinned specimen is pressed after applying a load against a plane-rotating disc such that the machine describes a circular wear pathway [11]. The machine has been used to evaluate the wear and friction properties of materials under pure sliding conditions reported in the following Figs. 4, 5 and 6. The wear testing machine has directly obtained these images. Fig. 4 Wear result of AZ91-CNT 0.5%

Fig. 5 Wear result of AZ91-CNT 1%

Fig. 6 Wear result of AZ91-CNT 1.5%

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6 Conclusion The powder metallurgy process followed by mechanical alloying was successfully applied to synthesize AZ91-CNT nanocomposites. Mechanical alloying through high-energy ball milling helps to improve homogeneous mixing and reduces the agglomeration of CNTs within the AZ91 matrix. The hardness and wear tests have revealed enhanced mechanical properties of AZ91-CNT composites due to the effect of mechanical alloying through ball milling. The results of the mechanical behavior reveal that an increasing percentage of up to 1 wt% CNTs in the AZ91 matrix leads to an improvement in 7.5% wear resistance. Increased percentage of CNT in the composite resulted in increased hardness as verified by the Rockwell hardness test. It was also seen that increase in CNT % from 1 to 1.5% showed an increase in wear which may be a result of the coagulation of carbon particles. The results suggest an increase in both hardness and wear resistance of the composite AZ91-CNT. Acknowledgments We would like to convey our sincere thanks to the Management of Reva University for providing us required infrastructure. Author Contributions All authors had approved the final version. Conflict of Interest The authors declare no conflict of interest.

References 1. H. Cao, M. Huang, C. Wang, S. Long, J. Zha, G. You, Research status and prospects of melt refining and purification technology of magnesium alloys. J. Magnesium Alloys 7, 370–380 (2019). https://doi.org/10.1016/j.jma.2019.07.002 2. I. Dinaharan, S.C. Vettivel, M. Balakrishnan, E.T. Akinlabi, Influence of processing route on microstructure and wear resistance of fly ash reinforced AZ31 magnesium matrix composites. J. Magnesium Alloys 7, 155–165 (2019). https://doi.org/10.1016/j.jma.2019.01.003 3. S.N.H. Mohamad Rodzi, H. Zuhailawati, B.K. Dhindaw, Mechanical and degradation behaviour of biodegradable magnesium–zinc/hydroxyapatite composite with different powder mixing techniques. J. Magnesium Alloys 7, 566–576 (2019). https://doi.org/10.1016/j.jma. 2019.11.003 4. J. Song, J. She, D. Chen, F. Pan, Latest research advances on magnesium and magnesium alloys worldwide. J. Magnesium Alloys 8, 1–41 (2020). https://doi.org/10.1016/j.jma.2020.02.003 5. T. Xu, Y. Yang, X. Peng, J. Song, F. Pan, Overview of advancement and development trend on magnesium alloy. J. Magnesium Alloys 7, 536–544 (2019). https://doi.org/10.1016/j.jma. 2019.08.001 6. K.N. Zhao, D.X. Xu, H.X. Li, J.S. Zhang, D.L. Chen, Microstructure and mechanical properties of Mg/Mg bimetal composites fabricated by hot-pressing diffusion and co-extrusion. Mater. Sci. Eng., A 764, (2019). https://doi.org/10.1016/j.msea.2019.138194 7. J.F. Nie, Y.M. Zhu, J.Z. Liu, X.Y. Fang, Periodic segregation of solute atoms in fully coherent twin boundaries. Science 340, 957–960 (2013). https://doi.org/10.1126/science.1229369 8. T.M. Pollock, Weight loss with magnesium alloys. Science 328, 986–987 (2010). https://doi. org/10.1126/science.1182848

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9. S. Pratap Singh Yadav, S. Ranganatha, G.M. Sandeep, S. Sharieff, Abrasive wear trends of non-conforming contact surfaces. Mater. Today: Proc. 5, 152–160 (2018). https://doi.org/10. 1016/j.matpr.2017.11.066 10. D. Gowda, D.C. Kumar, G.M. Sandeep, A. Parthasarathy, S. Chandrashekar, Tribological characterization of centrifugally cast graphite cast iron under dry and wet conditions. Mater. Today: Proc. 5, 145–151 (2018). https://doi.org/10.1016/j.matpr.2017.11.065 11. S.L. Xiang, M. Gupta, X.J. Wang, L.D. Wang, X.S. Hu, K. Wu, Enhanced overall strength and ductility of magnesium matrix composites by low content of graphene nanoplatelets. Compos. A Appl. Sci. Manuf. 100, 183–193 (2017). https://doi.org/10.1016/j.compositesa.2017.05.011

Re-design and Analysis of Brick Trolley S. M. Sanjay Kumar, S. Vinay Kumar, R. Manoj Bevoor, L. J. Chirayu, and A. Uday

Abstract Bricks are being widely used as construction materials for home, industrial, and commercial projects. The convectional trolleys have a major drawback that, it cannot carry heavy loads, requires more time to transport and requires more manual effort. It also requires more manual power and hence an increase in wages. An effort has been made to overcome the problem of Loading & unloading bricks from the trolley and transporting them from one place to other. This work contains the design and development of a trolley on the basis of creativity skills to perform multi-functions. It also contains modeling using COMSOL software and analysis of trolley for various loads using Ansys V19. This work concentrates on designing the trolley are load-carrying capacity and displacement due to loading. The overall design ensures the ability of the trolley to carry multiple bricks by a single worker and load the same to the truck at one instant of time. Keywords Bricks · Trolley · Von-Mises stress · Total tractive effort · Slider crank mechanism

1 Introduction A trolley is a small transport device used to move significant loads from one place to another. In most industries, hand trolleys are normally used to transport finished products or raw materials. Different kinds of trolleys exist and also the type used is commonly chosen supported what kind of material it will move. But these trolleys cannot use to move on rough surfaces and carry heavy loads. So, in such a place there is going to be a requirement of a trolley that reduces human efforts. In this work, the trolley is provided to hold the bricks and carry them from one place to another with less human effort. It additionally eases the movement of the trolley in the irregular surface, such as holes, bumps, etc. The research was carried out with different parameter, some are mentioned below.

S. M. Sanjay Kumar (B) · S. Vinay Kumar · R. Manoj Bevoor · L. J. Chirayu · A. Uday Department of Mechanical Engineering, SJBIT, Bengaluru 560060, Karnataka, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 C. S. Ramesh et al. (eds.), Recent Trends in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-2086-7_8

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V. Deshpande et al. [1] developed a mechanized transport system that shifts the fatigue to machine and brings effectiveness to human efforts. The selection of the appropriate system depends on the factors such as volumes to be handled, speed in handling, product characteristics (weight, size, shape) and nature of the product (hazardous, perishable, crushable). This work was successfully tested and implemented which is both economical and an affordable energy solution to material transport. K.V. Wankhade et al. [2] designed a transfer trolley mechanism for molten metal handling and pouring so as only one worker should be able to perform this operation. It will make this process more efficient and user-friendly. As the labor requirement is reduced so alternately the cost of production is also reduced. Materials are selected, analytical and mathematical calculations were calculated, some perimeters are assumed and some found out. Further, the Model is constructed using CAD software based on mathematical calculations. In the present work, trolleys for transporting are designed to move solid bricks from one place to another place without much effort. The designed trolley needs to be redesign to transfer the bricks automatically to any vehicle without much effort. Previous research did not show much work on the automation of the trolley.

2 Methodology • Bricks are unloaded at the construction site. • Usually, these bricks are unloaded manually into stocks and placed on the ground. • These bricks are manually carried by workers using brick hold and placed wherever needed. • The designed model consists of a latching mechanism. • It consists of two jaws, one is fixed and another is movable, attached at the base end of the trolley. • The trolley is taken manually at the stocks and the base is placed parallel to stocks. • The movable jaw is operated in order to hold the ricks and the trolley is slightly inclined so, that the bricks rest on the trolley. • Then the trolley is transported to the required place and the bricks are unloaded in the same manner in which it is loaded (Fig. 1). The revised design of trolley: • The revised design is both the jaws are moved to hold the bricks firmly. • The advantage over the previous design is that the revised trolley can carry a comparatively more volume of bricks with an overall length of 1 m. • It works on the principle of the Slider crank double lock mechanism (Figs. 2 and 3).

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Fig. 1 Previously designed trolley model

Fig. 2 Revised trolley model

3 Design Calculations 3.1 Calculation of Torque in Loaded Condition The loads on the trolley can be applied 100 kgs or multiple of 100 kgs. So, starting with the calculation of the torque required for moving the trolley.

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Fig. 3 Back view of trolley design

(i)

100 kg Load (50 Bricks) (Assuming 1 Brick = 2 kg)

Where, Gross Vehicle Weight(GVW) = 250 kg Rolling resistance(RR) = 250 ∗ 0.0125 RR = 3.125 kg Grade Resistance(GR) = 250 ∗ sin20 GR = 8.72 kg Force required to accelerate Trolley(FA) = (250 ∗ 0.5)/(9.81 ∗ 10) FA = 1.274 kg. Total Tractive Effort(TTE) = RR + GR + FA TTE = 3.125 + 8.72 + 1.274 TTE = 13.119 kg Tw = TTE ∗ Rw ∗ RF Where Rw = Radius of wheel/tyre Tw = Drive Wheel Torque MTT = Maximum Tractive Torque Tw = 13.119 ∗ 0.22 ∗ 1.3 = 3.75 kg-m as Tw < MTT The required torque will be transmitted to the ground and there will be no occurrence of slipping.

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(ii)

95

Similarly for 300 kg Load (150 Bricks)

As Tw < MTT 6.752 kg-m < 9.92 kg-m. The required torque will be transmitted to the ground and there will be no occurrence of slipping. Maximum loading capacity, when torque can be transmitted to ground. MTT = 9.92 kg-m. let Tw (max) = 9.5 kg-m (approx). TTE = Tw /(0.22 ∗ 1.3) TTE = 9.5/(0.22 ∗ 1.3) TTE = 33.216 kg TTE = RR + GR + FA   33.216 = GVW ∗ 0.0125 + sin 20 + 0.5/(9.81 ∗ 10) 33.216 = GVW ∗ (0.0125 + 0.0349 + 0.005096) 33.216 = GVW ∗ 0.005249 GVW = 632.82 kg. So, the maximum capacity will be (GVW − 150), that is, around 485 kg. So, according to the above calculations, the maximum load that can be carried by a trolley will be