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Lecture Notes in Mechanical Engineering

Ranganath M. SingariKaliyan MathiyazhaganHarish Kumar   Editors

Advances in Manufacturing and Industrial EngineeringSelect Proceedings of ICAPIE 2019

Lecture Notes in Mechanical Engineering

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Ranganath M. Singari • Kaliyan Mathiyazhagan •

Harish KumarEditors

Advances in Manufacturingand Industrial EngineeringSelect Proceedings of ICAPIE 2019

123

EditorsRanganath M. SingariDepartment of Mechanical EngineeringDelhi Technological UniversityNew Delhi, India

Harish KumarDepartment of Mechanical EngineeringNational Institute of Technology DelhiNew Delhi, India

Kaliyan MathiyazhaganDepartment of Mechanical EngineeringAmity School of Engineering andTechnologyNoida, India

ISSN 2195-4356 ISSN 2195-4364 (electronic)Lecture Notes in Mechanical EngineeringISBN 978-981-15-8541-8 ISBN 978-981-15-8542-5 (eBook)https://doi.org/10.1007/978-981-15-8542-5

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer NatureSingapore Pte Ltd. 2021This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whetherthe whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse ofillustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, andtransmission or information storage and retrieval, electronic adaptation, computer software, or by similaror dissimilar methodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe 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 thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

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Contents

A Review on the Fabrication of Surface Composites via Friction StirProcessing and Its Modeling Using ANN . . . . . . . . . . . . . . . . . . . . . . . 1Kartikeya Bector, Aranyak Tripathi, Divya Pandey, Ravi Butola,and Ranganath M. Singari

A Statistical Study of Consumer Perspective Towards the SupplyChain Management of Food Delivery Platforms . . . . . . . . . . . . . . . . . . 13Gangesh Chawla, Keshav Aggarwal, N. Yuvraj, and Ranganath M. Singari

Design of an Auxiliary 3D Printed Soft Prosthetic Thumb . . . . . . . . . . 27Akash Jain, Deepanshika Gaur, Chinmay Bindal, Ranganath M. Singari,and Mohd. Tayyab

Prediction of Material Removal Rate and Surface Roughness in CNCTurning of Delrin Using Various Regression Techniques and NeuralNetworks and Optimization of Parameters Using GeneticAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Susheem Kanwar, Ranganath M. Singari, and Vipin

Finding Accuracies of Various Machine Learning Algorithmsby Classification of Pulsar Stars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Abhishek Seth, Arjun Monga, Urvashi Yadav, and A. S. Rao

Robust Vehicle Development for Student Competitionsusing Fiber-Reinforced Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . 61Nikhil Sethi, Prabhash Chauhan, Shashwat Bansal,and Ranganath M. Singari

Study and Applications of Fuzzy Systems in Domestic Products . . . . . 77Vatsal Agarwal, Sunakshi, Rani Medhashree, Taruna Singh,and Ranganath M. Singari

v

Study of Process Parameters in Synergic MIG Welding a Review . . . . 89Rajat Malik and Mahendra Singh Niranjan

Comparative Study of Tribological Parameters of 3D Printed ABSand PLA Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Keshav Raheja, Ashu Jain, Chayan Sharma, Ramakant Rana, and Roop Lal

Study of Key Issues, Their Measures and Challengesto Implementing Green Practice in Coal Mining Industriesin Indian Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Gyanendra Prasad Bagri, Dixit Garg, and Ashish Agarwal

A Brief Review on Machining with Hybrid MQL Methods . . . . . . . . . 123Rahul Katna, M. Suhaib, Narayan Agrawal, and S. Maji

Analysis of Interrelationship Among Factors for EnhancedAgricultural Waste Utilization to Reduce Pollution . . . . . . . . . . . . . . . 135Nikhil Gandhi, Abhishek Verma, Rohan Malik, and Shikhar Zutshi

Enhancement of Mechanical Properties for Dissimilar Welded Jointof AISI 304L and AISI 202 Austenitic Stainless Steel . . . . . . . . . . . . . 145Yashwant Koli, N. Yuvaraj, Vipin, and S. Aravindan

Effect of 3D Printing on SCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Shallu Bhasin, Ranganath M. Singari, and Harish Kumar

Seasonal Behavior of Trophic Status Index of a Water Body,Bhalswa Lake, Delhi (India) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165Sumit Dagar and S. K. Singh

Seasonal Variation of Water Quality Index of an Urban Water BodyBhalswa Lake, Delhi (India) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Sumit Dagar and S. K. Singh

Material Study and Fabrication of the Next-Generation UrbanUnmanned Aerial Vehicle: Aarush X2 . . . . . . . . . . . . . . . . . . . . . . . . . 191Rishabh Dagur, Krovvidi Srinivas, Vikas Rastogi,Prakash Sesha, and N. S. Raghava

Intelligent Transport System: Classification of Traffic Signs UsingDeep Neural Networks in Real Time . . . . . . . . . . . . . . . . . . . . . . . . . . 207Anukriti Kumar, Tanmay Singh, and Dinesh Kumar Vishwakarma

Fabrication of Aluminium 6082–B4C–Aloe Vera Metal MatrixComposite with Ultrasonic Machine Using Mechanical Stirrer . . . . . . 221Manish Kumar Chaudhary, Ashutosh Pathak, Rishabh Goyal,Ramakant Rana, and Vipin Kumar Sharma

vi Contents

A Fuzzy AHP Approach for Prioritizing Diesel Locomotive Shedsa Case Study in Northern Railways Network . . . . . . . . . . . . . . . . . . . . 231Reetik Kaushik, Yasham Raj Jaiswal, Roopa Singh, Ranganath M. Singari,and Rajiv Chaudhary

Operation of Big-Data Analytics and Interactive Advertisementfor Product/Service Delineation so as to Approach Its Customers . . . . 247Harshmit Kaur Saluja, Vinod Kumar Yadav, and K. M. Mohapatra

Effect of Picosecond Laser Texture Surface on TribologicalProperties on High-Chromium Steel Under Non-lubricatedConditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257Sushant Bansal, Ayush Saraf, Ramakant Rana, and Roop Lal

A Statistical Approach for Overcut and Burr Minimization DuringDrilling of Stir-Casted MgO Reinforced Aluminium Composite . . . . . . 269Anmol Gupta, Surbhi Lata, Ramakant Rana, and Roop Lal

Study and Design Conceptualization of Compliant Mechanismsand Designing a Compliant Accelerator Pedal . . . . . . . . . . . . . . . . . . . 285Harshit Tanwar, Talvinder Singh, Balkesh Khichi, R. C. Singh,and Ranganath M. Singari

Numerical Study on Fracture Parameters for Slit Specimensfor Al2124 and Micro-alloyed Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . 297Pranjal Shiva and Sanjay Kumar

Different Coating Methods and Its Effects on the Tool Steels:A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307Sourav Kumar, Kanwarjeet Singh, Gaurav Arora, and Swati Varshney

Evaluation of Work-Related Stress Amongst Industrial Workers . . . . 315Anuradha Kumari and Ravindra Singh

Exergoeconomic and Enviroeconomic Analysis of Flat PlateCollector: A Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329Prateek Negi, Ravi Kanojia, Ritvik Dobriyal, and Desh Bandhu Singh

Lead–Lag Relationship Between Spot and Futures Prices of IndianAgri Commodity Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339Raushan Kumar, Nand Kumar, Aynalem Shita, and Sanjay Kumar Pandey

Learnify: An Augmented Reality-Based Application for Learning . . . . 349Himanshi Sharma, Nikhil Jain, and Anamika Chauhan

Thermal Analysis of Friction Stir Welding for Different ToolGeometries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361Umesh Kumar Singh, Avanish Kumar Dubey, and Ashutosh Pandey

Contents vii

Analysis of Electrolyte Flow Effects in Surface Micro-ECG . . . . . . . . . 371Dhruv Kant Rahi, Avanish Kumar Dubey, and Nisha Gupta

Investigate the Effect of Design Variables of Angular Contact BallBearing for the Performance Requirement . . . . . . . . . . . . . . . . . . . . . . 381Priya Tiwari and Samant Raghuwanshi

Effect of Flow of Fluid Mass Per Unit Time on Life Cycle ConversionEfficiency of Double Slope Solar Desalination Unit Coupledwith N Identical Evacuated Tubular Collectors . . . . . . . . . . . . . . . . . . 393Desh Bandhu Singh, Navneet Kumar, Anuj Raturi, Gagan Bansal,Akhileshwar Nirala, and Neeraj Sengar

Micro-milling Processes: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403Kriti Sahai, Audhesh Narayan, and Vinod Yadava

Strategic Enhancement of Operating Efficiency and Reliabilityof Process Steam Boilers System in Industry . . . . . . . . . . . . . . . . . . . . 413Debashis Pramanik and Dinesh Kumar Singh

A Step Towards Responsive Healthcare Supply Chain Management:An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431Shashank Srivastava, Dixit Garg, and Ashish Agarwal

Designing of Fractional Order Controller Using SQP Algorithmfor Industrial Scale Polymerization Reactor . . . . . . . . . . . . . . . . . . . . . 445D. Naithani, M. Chaturvedi, P. K. Juneja, and Vivek Joshi

Additive Manufacturing in Supply Chain Management:A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455Archana Devi, Kaliyan Mathiyazhagan, and Harish Kumar

A Step Towards Next-Generation Mobile Communication:5G Cellular Mobile Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . 465Ayush Kumar Agrawal and Manisha Bharti

Efficacy and Challenges of Carbon Trading in India:A Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477Naveen Rai and Meha Joshi

Micro-structural Investigation of Embedded Cam Tri-flute Tool PinDuring Friction Stir Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485Nadeem Fayaz Lone, Arbaz Ashraf, Md Masroor Alam, Azad Mustafa,Amanullah Mahmood, Muskan Siraj, Homi Hussain, and Dhruv Bajaj

Design and FEM Analysis of Connecting Rod of DifferentMaterials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493Sanjay Kumar, Vipin Verma, and Neelesh Gupta

viii Contents

Numerical Study on Heat Affected Zone and Material Removal Rateof Shape Memory Alloy in Wire Electric Discharge Machining . . . . . . 509Deepak Kumar Gupta, Avanish Kumar Dubey, and Alok Kumar Mishra

A Hybrid Multi-criteria Decision-Making Approach for Selectionof Sustainable Dielectric Fluid for Electric Discharge MachiningProcess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519Md Nadeem Alam, Zahid A. Khan, and Arshad Noor Siddiquee

Preference Selection Index Approach as MADM Method forRanking of FMS Flexibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529Vineet Jain, Mohd. Iqbal, and Ashok Kumar Madan

Impact of Additive Manufacturing in Value Creation, Methods,Applications and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543Rishabh Teharia, Gulshan Kaur, Md Jamil Akhtar,and Ranganath M. Singari

3D Printing: A Review of Material, Properties and Application . . . . . 555Gulshan Kaur, Rishabh Teharia, Md Jamil Akhtar,and Ranganath M. Singari

Effect of Infill Percentage on Vibration Characteristicof 3D-Printed Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565Pradeep Kumar Yadav, Abhishek, Kamal Singh, and Jitendra Bhaskar

Study of Slender Carbon Fiber-Reinforced Columns Filledwith Concrete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575Utkarsh Roy, Shubham Khurana, Pratikshit Arora, and Vipin

Factors Affecting Import Demand in India: A Principal ComponentAnalysis Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585Khyati Kathuria and Nand Kumar

Theoretical and Statistical Analysis of Inventory and WarehouseManagement in Supply Chain Management—A Case Studyon Small-Scale Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597Mahesh R. Latte and Channappa M. Javalagi

Evaluation of Separation Efficiency of a Cyclone-Type OilSeparator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609Ujjwal Suri, Shraman Das, Utkarsh Garg, and B. B. Arora

Energy Analysis of Double Evaporator Ammonia Water VapourAbsorption Refrigeration System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619Deepak Panwar and Akhilesh Arora

Blockchain Technology as a Tool to Manage Digital Identity:A Conceptual Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635Ruchika Singh Malyan and Ashok Kumar Madan

Contents ix

Evolution in Micro-friction Stir Welding . . . . . . . . . . . . . . . . . . . . . . . 649Nadeem Fayaz Lone, Md Masroor Alam, Arbaz Ashraf,Amanullah Mahmood, Nabeel Ali, Dhruv Bajaj, and Soumyashri Basu

Traffic Noise Modelling Considering Traffic Compositionsat Roundabouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657Anupam Thakur and Ramakant Rana

Hydrogen Embrittlement Prevention in High Strength Steelsby Application of Various Surface Coatings-A Review . . . . . . . . . . . . . 673Sandeep Kumar Dwivedi and Manish Vishwakarma

Commencement of Green Supply Chain Management Barriers:A Case of Rubber Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685Somesh Agarwal, Mohit Tyagi, and R. K. Garg

Estimation of Critical Key Performance Factors of Food Cold SupplyChain Using Fuzzy AHP Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 701Neeraj Kumar, Mohit Tyagi, and Anish Sachdeva

A Short Review on Machining with Ultrasonic MQL Method . . . . . . . 713Rahul Katna, M. Suhaib, Narayan Agrawal, and S. Maji

Professional Values and Ethics: Challenges, Solutionsand Different Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721Shalini Sharma

Design of 3D Printed Fabric for Fashion and FunctionalApplications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729Arpit Singh, Pradeep Kumar Yadav, Kamal Singh, Jitendra Bhaskar,and Anand Kumar

Fabrication and Characterization of PVA-Based Films Cross-Linkedwith Citric Acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737Naman Jain, Gaurang Deep, Ashok Kumar Madan, Madhur Dubey,Nomendra Tomar, and Manik Gupta

Characterization of Bael Shell (Aegle marmelos) Pyrolytic Biochar . . . 747Monoj Bardalai and D. K. Mahanta

Metal Foam Manufacturing, Mechanical Properties and ItsDesigning Aspects—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761Rahul Pandey, Piyush Singh, Mahima Khanna, and Qasim Murtaza

Emerging Trends in Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . 771Yash Agarwal and K. A. Nethravathi

Selection of Best Dispatching Rule for Job Sequencing UsingCombined Best–Worst and Proximity Index Value Methods . . . . . . . . 783Shafi Ahmad, Ariba Akber, Zahid A. Khan, and Mohammed Ali

x Contents

Thermal Performance Investigation of a Single Pass SolarAir Heater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793Ovais Gulzar, Adnan Qayoum, and Rajat Gupta

Modelling of Ambient Noise Levels in Urban Environment . . . . . . . . . 807S. K. Tiwari, L. A. Kumaraswamidhas, and N. Garg

Development and Characterizations of ZrB2–SiC CompositesSintered Through Microwave Sintering . . . . . . . . . . . . . . . . . . . . . . . . 815Ankur Sharma and D. B. Karunakar

Characterization of Ni-Based Alloy Coating by ThermalSpraying Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825Manmeet Jha, Deepak Kumar, Pushpendra Singh, R. S. Walia,and Qasim Murtaza

A Review on Solar Panel Cleaning Through ChemicalSelf-cleaning Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 835Ashish Jaswal and Manoj Kumar Sinha

Investigations on Process Parameters of Wire Arc AdditiveManufacturing (WAAM): A Review . . . . . . . . . . . . . . . . . . . . . . . . . . 845Mayank Chaurasia and Manoj Kumar Sinha

A State-of-the-Art Review on Fused Deposition ModellingProcess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855Kamal Kishore and Manoj Kumar Sinha

3D Modelling of Human Joints Using Reverse Engineeringfor Biomedical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865Deepak Kumar, Abhishek, Pradeep Kumar Yadav, and Jitendra Bhaskar

Institutional Distance in Cross-Border M&As: Indian Evidence . . . . . 877Sakshi Kukreja, Girish Chandra Maheshwari, and Archana Singh

Synthesis and Characterization of PVDF/PMMA-Based PiezoelectricBlend Membrane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 889Ashima Juyal and Varij Panwar

Comparative Study of Retrofitted Columns Using AbaqusSoftware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 897Geeta Singh, Tarun Shokeen, and Vidrum Gaur

Optimal Pricing and Procurement Decisions for Itemswith Imperfect Quality and Fixed Shelf Life Under Selling PriceDependent and Power Time Pattern Demand . . . . . . . . . . . . . . . . . . . 907Sonal Aneja and K. K. Aggarwal

Experimental Analysis of Portable Optical Solar Water Heater . . . . . . 925Hasnain Ali, Ovais Gulzar, K. Vasudeva Karanth,Mohammad Anaitullah Hassan, and Mohammad Zeeshan

Contents xi

CO2 Laser Micromachining of Polymethyl Methacrylate (PMMA):A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 939Shrikant Vidya, Reeta Wattal, Lavepreet Singh, and P. Mathiyalagan

Design of Delay Compensator for a Selected Process Model . . . . . . . . 947Oumayma Benjeddi, M. Chaturvedi, P. K. Juneja, G. Yadav, Vivek Joshi,and R. Mishra

Fly Ash, Rice Husk Ash as Reinforcement with Aluminium MetalMatrix Composite: A Review of Technique, Parameterand Outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 953Jagannath Verma and Harish Kumar

Optimization of CNC Lathe Turning: A Review of Technique,Parameter and Outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 963Vivek Joshi and Harish Kumar

Innovations and Future of Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . 975Ayush Kumar Agrawal, Pritam Pidge, Manisha Bharti, M. Prabhat Dev,and Prashant Kaduba Kedare

Optimization of EDM Process Parameters: A Review of Technique,Process, and Outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 981Akash Gupta and Harish Kumar

Impact Behavior of Deformable Pin-Reinforced PU FoamSandwich Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997Shivanku Chauhan, Mohd. Zahid Ansari, Sonika Sahu, and Afzal Husain

Sensitivity Improvement of Piezoelectric Mass Sensing CantileversThrough Profile Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1007Shivanku Chauhan, Mohd. Zahid Ansari, Sonika Sahu, and Afzal Husain

Current Status, Applications, and Factors Affecting Implementationof Additive Manufacturing in Indian Healthcare Sector:A Literature-Based Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015Bhuvnesh Chatwani, Deepanshu Nimesh, Kuldeep Chauhan,Mohd Shuaib, and Abid Haleem

System Optimization for Economic and Sustainable Productionand Utilization of Compressed Air (A Case Study in AsbestosSheet Manufacturing Plant) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1031Debashis Pramanik and Dinesh Kumar Singh

Investigating the Prospects of E-waste and Plastic Wasteas a Material for Partial Replacement of Aggregates in Concrete . . . . 1047Abhishek Singh, Ahmad Sahibzada, Deepak Saini, and Susheel Kumar

xii Contents

Investigation of Combustion, Performance and Emissionof Aluminium Oxide Nanoparticles as Additives in CI Engine Fuels:A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055Manish Kumar, Naushad A. Ansari, and Samsher

A Review of CI Engine Performance and Emissions with GrapheneNanoparticle Additive in Diesel and Biodiesel Blends . . . . . . . . . . . . . . 1065Varun Kr Singh, Naushad A. Ansari, and Akhilesh Arora

Synthesis and Study of a Novel Carboxymethyl Guar Gum/Polyacrylate Polymeric Structured Hydrogel for AgriculturalApplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1073Khushbu, Ashank Upadhyay, and Sudhir G. Warkar

Artificial Neural Network (ANN) for Forecasting of Flood at Kasolin Satluj River, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1085Abhinav Sharma and Anshu Sharma

Sewage Treatment Using Alum with Chitosan: A ComparativeStudy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1095Jaya Maitra, Athar Hussain, Mayank Tripathi, and Mridul Sharma

Automatic Plastic Sorting Machine Using Audio Wave Signal . . . . . . . 1111S. M. Devendra Kumar, S. Prashanth, and Rani Medhashree

GSM Constructed Adaptable Locker Safety Scheme by Meansof RFID, PIN Besides Finger Print Expertise . . . . . . . . . . . . . . . . . . . . 1123S. M. Devendra Kumar, B. Manjula, and Rani Medhashree

Low-Voltage Squarer–Divider Circuit Using Level Shifted FlippedVoltage Follower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1131Swati Yadav and Bhawna Aggarwal

Memristor-Based Electronically Tunable Unity-Gain Sallen–KeyFilters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1141Bhawna Aggarwal, Manshul Arora, Marsheneil Koul,and Maneesha Gupta

Influence of Target Fields on Impact Stresses and Its Deformationsin Aerial Bombs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1153Prahlad Srinivas Joshi and S. K. Panigrahi

A Review of Vortex Tube Device for Cooling Applications . . . . . . . . . 1161Sudhanshu Sharma, Kshitiz Yadav, Gautam Gupta, Deepak Aggrawal,and Kulvindra Singh

Implementation of Six-Sigma Tools in Hospitality Industry:A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1171Nishant Bhasin, Harkrit Chhatwal, Aditya Bassi, and Shubham Sharma

Contents xiii

Impact of Integrating Artificial Intelligence with IoT-Enabled SupplyChain—A Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . . 1183Ranjan Arora, Abid Haleem, P. K. Arora, and Harish Kumar

Experimental Study for the Health Monitoring of Milling ToolUsing Statistical Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1189Akanksha Chaudhari, Pavan K. Kankar, and Girish C. Verma

PVT Aware Analysis of ISCAS C17 Benchmark Circuit . . . . . . . . . . . 1199Suruchi Sharma, Santosh Kumar, Alok Kumar Mishra, D. Vaithiyanathan,and Baljit Kaur

xiv Contents

About the Editors

Dr. Ranganath M. Singari is a Professor in the Department of Mechanical,Production & Industrial Engineering and heads the Department of Design, DelhiTechnological University, India. He is a graduate in Industrial ProductionEngineering from Karnataka University. He completed his M.Tech in ComputerTechnology & Applications and Ph.D. from the Department of ProductionEngineering from University of Delhi, India. He has more than 60 internationalpublications in conference and reputed journals. He is also a reviewer for reputedjournals. Dr. Singari has organised several international conferences, seminars/workshops, industry-institute interactions and 6 FDP/SDP/STTP. He also serves asChairman, Production Engineering, Skill India Programme, DTTE, Delhi. He is anexpert member of several selection committees for technical, teaching andadministrative positions. His research interest is materials, manufacturing, industrialmanagement, production management, CAD/CAM, supply chain management,multi-criteria decision making and sustainable lean manufacturing. He has 25 yearsof research and teaching experience.

Dr. Kaliyan Mathiyazhagan is currently working as an Associate Professor in theDepartment of Mechanical Engineering, Amity University, India. He pursued hisPh.D. from the Department of Production Engineering, National Institute ofTechnology, Tiruchirappalli, Tamil Nadu. He was also a visiting research fellow atthe University of Southern Denmark. He has more than 60 international publicationsin reputed journals and one of his papers received the best paper award in NCAME2019, NIT Delhi. Dr. K. Mathiyazhagan is an associate editor of Environment,Development and Sustainability. He is also an editorial member of more than5 international journals. He has served as a guest editor for several special issues ininternational journals and is an active reviewer of more than thirty reputed inter-national journals. His research interest is green supply chain management, sustain-able supply chain management, multi-criteria decision making, third party logisticprovider, sustainable lean manufacturing, public distribution system, and Lean SixSigma. He has more than 10 years of research and teaching experience.

xv

Dr. Harish Kumar is currently working as an Assistant Professor at the NationalInstitute of Technology, Delhi. He has more than 15 years of research and academicexperience and has served as a scientist at different grade in CSIR - NationalPhysical Laboratory, India (NPLI). He has been an active researcher in the area ofmechanical measurement and metrology. He has worked as a guest researcher at theNational Institute of Standards and Technology, USA in 2016. He has beeninstrumental in the ongoing redefinition of the kilogram in India. He has authoredmore than 70 publications in peer reviewed journals and conferences. He is anactive reviewer of many reputed journals related to measurement, metrology andrelated areas. He has served as a guest editor of different peer reviewed journals.

xvi About the Editors

A Review on the Fabrication of SurfaceComposites via Friction Stir Processingand Its Modeling Using ANN

Kartikeya Bector, Aranyak Tripathi, Divya Pandey, Ravi Butola,and Ranganath M. Singari

Abstract Friction Stir Processing (FSP) is a surface modification and surfacecomposite fabrication technique that was first theorized and demonstrated in 2002.Since then, it has grown exponentially in the industry due to its efficiency, ease ofusability and various other advantages over conventional surface modification andfabrication processes. Artificial Neural Networks provide a computationalmodel thatcan handle complex relationships between the various determinants involved in FSPand the effect they have on the final output. ANN has been used extensively to predictthe impact of various FSP determinants and hence model the most efficient valuesof these determinants for various base metals. This paper has tried to encapsulatethe plethora of research done on the optimization of FSP determinants using ANNarchitecture.

Keywords FSP ·MMCs · ANN

1 Introduction

Most metal matrix composites, reinforced with ceramic phases or another desiredmetal, exhibit a higher elastic modulus, higher strength, higher Vickers hardness andbetter resistance to fatigue, creep andwear than the basemetal. Thismakes them suit-able for the aerospace and automobile industries. Since the ceramic reinforcementmaterials introduced in the base metal matrix are non-deformable and brittle, one ofthe drawbacks of such composites, especially in the case of such ceramic additives, isthe loss of crucial properties of the base material—ductility and toughness. Thus, thecomposites formed have a limited application. The solution for this is sought from the

K. Bector · A. Tripathi · D. Pandey · R. Butola (B)Departments of Mechanical Engineering, Delhi Technological University, New Delhi, Delhi110042, Indiae-mail: [email protected]

R. M. SingariDepartments of Production and Industrial Engineering, Delhi Technological University, NewDelhi, Delhi 110042, India

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021R. M. Singari et al. (eds.), Advances in Manufacturing and Industrial Engineering,Lecture Notes in Mechanical Engineering,https://doi.org/10.1007/978-981-15-8542-5_1

1

2 K. Bector et al.

fact that for a majority of applications, the surface properties of the composite play amajor role. Following this, Friction Stir Processing (FSP) was ideated and pioneeredbyMishra et al. [1] in 2002. This technology stems fromFriction StirWelding (FSW)that was actualized back in 1991. It was derived from the process of using friction toweld joints even in aluminium, titanium and various other alloys. FSP is generallyutilized for lightweight and flexible metals like aluminium and magnesium, but onlyafter the base metal has experienced FSP to acquire certain desirable properties andmake up for their absence of strength and hardness. Chaudhary et al. [2] studied theconsequences of using FSP on different alloys like Mg4Y3Nd(WE43), Mg–ZrSiO4–N2O3, Al–Si hypoeutectic A356 alloy, 5210 steel (WC-12% CO coated). The differ-ence in the properties of friction stir processed alloyswere observed as the processingwas executed at different angles and speeds. A variety of ingenious materials may beused as reinforcement while preparing friction stir processed composites. The effectsand properties of reinforcing materials such as Silicon Carbide, Graphite, Fly ash,Rice husk ash and boron halide were examined in detail by Butola et al. [3] for thepreparation of surface composites using FSP. Properties like corrosion behaviour,tensile strength, hardness, wear-resistance and so forth were studied rigorously andsummarized.

There exist several surface modification techniques like Laser Surface Engi-neering [4], high-energy electron beam irradiation [5], high-energy laser melt treat-ment [6], plasma spraying [7], stir casting [8], etc. The above-described techniquesfor the formation of surface composites rely on liquid-phase processing at hightemperatures. Because of the nature of the processing, it is inescapable that an inter-facial reaction occurs between the reinforcement and the metal matrix. Some otherimpeding phases may also be formed. The aforementioned problems may be miti-gated by carrying out the processing at a temperature below the melting point of thesubstrate.

Artificial neural network is an innovative prediction model that utilizes existingdata to train an intuitive network of neurons so that accurate complex predictionscan be made. Okuyucu et al. [9] pioneered the use of ANN in friction stir welding.An ANN model was created for the simulation and analysis of the interrelation-ship of FSW determinants and the mechanical properties of the aluminium plates.Similarly, ANN models considering various FSP determinants were developed andimplemented on friction stir processed alloys of aluminium and magnesium.

2 Principle and Effect of Parameters of FSP

Friction Stir Processing involves the heating and plasticization of the substrate andreinforcement material due to the friction created by the tool (with or without theprotruding mandrel). FSP can be carried out on a conventional FSW machine [10].During FSP, a non-consumable tool turns at a high RPM and gradually slides intothe workpiece while applying a power pivotally until the shoulder of the instrumentinteracts with the outside of the workpiece, which brings about erosion. The rotating

A Review on the Fabrication of Surface Composites via Friction … 3

Fig. 1 FSP for frictional modification of surface layers, a process diagram, b tool design [2], c avariety of mandrel designs of tools for FSP [11]

tool then moves along the workpiece in the desired direction of FSP. A substantialamount of heat is engendered due to traction between the shoulder of the tool andthe workpiece. The plasticized and heated material is forced along the modificationline and underneath the back-up rim to the end of the tool, where it’s compacted andblended because of severe deformation before it cools. FSP is a versatile method thatcan be used for manufacturing, modification as well as fabrication of materials withspecial properties (Fig. 1).

Moreover, FSP is an eco-friendly process as the heat energy required is generatedthrough friction [12]. This leads to the formation of a dynamically recrystallized finegrain structure. Friction stir processed areas can be generated to the depths of 0.5–50mm,with a progressive evolution from a fine-grained, thermodynamically workedmicrostructure to the elementary original microstructure [13]. The processing regionin FSP, like FSW, is usually categorized into a thermo-mechanically affected zone(TMAZ), a stir zone (SZ), heat-affected zone (HAZ) and base metal zone(BM) [14,15]. The SZ undergoes acute plastic deformation and primarily consists of homo-geneously refined grains which are equiaxial and whose dimensions are contractedmonumentally in comparison to the principal metal. During recrystallization, theformation of grains is promoted by the particle-stimulated nucleation when particlesof the reinforcement material are introduced to the metal matrix. During the dynamicrecrystallization process, the uniform dispersion of fine particles can inhibit graingrowth. This is in accordance with the Zener–Hollomanmechanism. This occurs dueto the pinning action on the grain boundaries leading to a substantial ameliorationof the microstructure [16]. The factors affecting the FSP modified substrate are tooltraversal speed, tilt angle of tool, RPM, tool plunge depth and dimensions of the tool[12].

4 K. Bector et al.

2.1 Impact of Process Determinants

The most important determinants are the RPM and the speed of traversal of the tool.This is because they directly affect the addition of heat and the flow of the plasticizedmaterial during FSP. This drastically influences the microstructure and hence, themechanical properties of the processed material. A higher speed of rotation, coupledwith a lower speed of traversal, leads to more heat being generated in the processingregion, which in itself becomes larger. This leads to a better-refined microstructureand an increase in the hardness [17–20]. A lower speed of traversal also helps tocontrol unusual grain growth [21]. Moreover, a higher speed of rotation of the tool orlower speed of traversal ensures that a higher augmentation of heat and more plasticdeformation is achieved. This becomes significant for the mixture of reinforcedparticles and the base metal matrix.

Multi-pass FSP has been proven to better the material properties by aiding theplastic deformation of processed materials. Increasing the number of passes andreversing the direction of rotation of the tool with every subsequent pass ensures thatthe composites manufactured by multi-pass FSP have a progressively uniform phasedispersion and strengthening because of thoroughly propagated in situ reactionsas compared to single-pass FSP [22]. The procedure may be designed accordinglyby selecting the percentage of overlap viz. 5, 10, 25, 50, 75% and thus desirabledimensions of the modified surface may be obtained [12].

2.2 Impact of Tool (Pin) Geometry

Tools used to stir thematerial in FSPmay bewith orwithout amandrel and are usuallynon-consumable [23]. A stir tool is usually used with the mandrel and the mandrel-less tools are used for either modifying the surface of the material or processing ofreinforcement material into the native metal during the fabrication of composites.The tool size and the geometry of the tool pin significantly affect the amount of heatproduced aswell as the flowof thematerial during processing.A large shoulder diam-eter of the tool results in the frictional heat being more concentrated. Subsequently,the particles in the second phase are refined better and thus the microstructure isalso more stable. Thus, the influence of pin profile is crucial and this results in thetemperature being the lowest when a conical pin is plunged into the material. Threedifferent pin profiles were studied by Butola et al. [11] by observing their effecton SiC, RHA and B4C-reinforced composites with AA7075 as the native metal.The coda showed that in the stir zone, which had also decreased in size, the mosthomogeneous distribution of reinforcement particles was seen in the case of a squaremandrel.

A Review on the Fabrication of Surface Composites via Friction … 5

2.3 Fabrication of Surface Composites

Many methods have been explored by various researchers to fabricate compositescontaining reinforcement particles. High modulus of elasticity, high strength, etc.are some of the inherent properties possessed by metal matrix composites. Surfacecomposites may be fabricated from these to enhance the surface properties of thesecomposites. The main challenge affecting this has been the introduction of theseparticles in the base metal during FSP. Apart from casting [24], a number of othermethods were also tried. The SiC powder was mixed with methanol to form a paste,which was, in turn, applied evenly onto the surface of the workpiece before FSP. But,the particles slipped easily and were splashed out of the surface due to the rotation ofthe tool. This leads to an irregular distribution of the reinforced phases and inefficientuse of material [1]. Some other ways to incorporate the reinforced particles in thematrix include making grooves on the surface of the base metal plates and addingthe particles in it. To prevent splashing, the grooves are pre-processed with a pin-lesstool using FSP [25, 26]. Similarly, blind holes may be drilled into the surface ofthe workpiece [27]. In both of these cases, final processing via FSP ensures that theparticles are uniformly dispersed in the metal matrix and the homogeneity in thedispersion of the strengthening phases vastly improves the properties of the surfacecomposite hence formed.

Liquid-phase techniques like laser cladding and plasma spraying are often usedfor this purpose. Butola et al. [8] used stir casting for the introduction of natural fibreslike bagasse, banana and jute to formmetal matrix composites and studied their effecton the mechanical properties of the base metal. In another study, Butola et al. [28]used stir casting and ball milling to fabricate and refine MMCs and study the effectof Groundnut Shell Ash (GSA), Rice Husk Ash (RHA) and ash-forms of some othernatural fibres as reinforcement. Due to the formation of a liquid-phase in the above-mentioned techniques, the deleterious reactions mentioned at the beginning of theresearch may happen. Using FSP for the same will ensure that a finely distributedphase of strengthening particles may be obtained while keeping the SZ in a solid-state. This effectively prevents the formation of detrimental phases and any unwantedinterfacial reactions. The commonly added reinforcement materials in FSP includeSiC, B4C, GNPs and Al2O3.

3 Artificial Neural Networks

Artificial neural network, popularly known as ANN, is a biology-inspired architec-ture of nodes (neurons) that are extensively interconnected [29]. It is a complexlearning model that trains on a set of data, analyzes and learns the pattern followedin it and then predicts the result of a similar dataset. It processes the informationin the datasets using a connectionist approach and multiple functions are run on itsimultaneously. Synapses link neurons and a weight factor is associated with each of

6 K. Bector et al.

Table 1 The inputs and outputs used by some previous works [31]

Inputs Outputs References

Welding speed (WS)Rotational speed (RPM)

Yield strength (YS), Length variation [9]

WS, RPM Tensile shear force, Hardness [32]

WS, RPM Tensile strength (TS), YS, Elongation [33]

WS, RPM, Axial force (F) TS [34]

WS, RPM, Tool shoulder diameter TS [29]

them. ANNs are data processing models that mirror the role of the biological matrix,made out of neurons and are utilized to understand convoluted capacities in differentapplications by determining the nonlinear relationship between the involved, influ-ential determinants and the output(s) obtained. The model has three layers viz. input,hidden and output layers. The input layer comprises of all the input factors. Data,via the input layer, is then processed through one or more hidden layers and thecorresponding output vector is calculated in the final layer. One of the most popularlearning algorithms is the backpropagation algorithm [30].Oneof the primaryhurdleswhile constructing an ANN model is to choose an appropriate network framework,which includes the activation function and the number of neurons in the hiddenlayer. Largely, tentation is used for the same. Since its introduction, ANN has beenused by various researchers to study the effect of various determinants on a processunder scrutiny. Friction Stir Processing is one such process. Since the advent ofthis processing method, the variables that control the resultant surface compositeformed, have been closely studied. Since there is no explicit correlation for esti-mating target determinants based on the input factors, usually target determinantsare modeled by a three-layer perceptron ANN using data obtained from mechanicaland microstructural experiments (Table 1).

Neural architecture with the following [35]:

• Input has r determinants• Output has s determinants• p—inputs• w—weight matrix• b—bias vectors• f—transfer function in neurons• a—transfer functions in outputs.

Training of a neuron is carried out by multiplying the input vector with a vectorof weights, followed by the addition of a bias vector. The result of this processingis then fed into the hidden layer. The sum of all the inputs is then fed into a transferfunction. The output thus obtained is the output of the neural network. This outputis then compared with the corresponding experimental values obtained. Due to thedifference in the expected and practical values, an error vector is generated. In casethis error value exceeds the acceptable error limit, the output is propagated back

A Review on the Fabrication of Surface Composites via Friction … 7

Fig. 2 Tangent sigmoid(Tansig) transfer function[31]

through the network and appropriate corrections in the weights and biases are madetill the desirable values are attained.

Logarithmic sigmoid (Logsig) transfer function:

ψ(x) = 1

1+ e−x(1)

Linear transfer function (Fig. 2):

χ(x) = linear(x) (2)

3.1 Training of ANN

The dataset that is available is usually split into two parts, usually in a 3:1 ratio [36].The bigger of the two datasets are used to train the ANN, while the other one acts asthe testing dataset. The final outputs obtained are compared with the expected values,error calculated and in case the error exceeds the permitted limit, the output is sentback through the network and required adjustments in the weights and biases aremade. The aim of the feed-forward backpropagation (BP) algorithm is to minimizethe sum of the mean squared errors obtained between the calculated and practicalvalues of output and the minimization is achieved via the gradient descent method.BP is one of the most efficient algorithms for the optimization of the weights andbiases of a multi-layer supervised feed-forward network.

8 K. Bector et al.

3.2 Implementation of ANN

There are many input factors in a neural network, changing which will, in turn,change the method of operation and the overall precision and processing speed ofthe network [37]. Some of these include but are not limited to the number of neuronsin each hidden layer, hidden layers, the bias used and the rate of training of thenetwork. As the count of hidden layers and nodes(neurons) in each layer are crucialin the overall functioning and performance of theANN (act as the primary processingentity of the network), they are chosen after careful consideration. They are usuallychosen by tentation because of the lack of a fixed formula to determine the same.Increasing them does not always lead to better performance in terms of the speed andaccuracy of the network. In fact, it increases the complexity of the network, which inturn, tends to slow down the network after a certain limit. An unstable network maybe obtained if the rate of training is increased or decreased beyond a certain limit. Allthe input and output determinants are normalized to prevent them from scattering.This means that the values of these determinants are divided by the maximum valueand hence reduced to a value between 0 and 1. This decreases the scattering of thedeterminants.

3.3 Performance Evaluation of ANN

A lot of statistical models are at one’s disposal to determine the performance ofany neural network [26]. The most common ones include the Pearson coefficient ofcorrelation (PCC) and mean relative error (MRE). Their equations are as follows:

Where,

PCC =∑n

i=1

(fEXP,i − FEXP

)(fANN,i − FANN

)

√∑n

i=1

((fEXP,i − FEXP

)2(fANN,i − FANN

)2) (3)

MRE = 1

n

n∑

i=1

∣∣ fANN,i − fEXP,i

∣∣ × 100

fEXP,i(4)

FEXP = 1

n

n∑

i=1

fEXP,i , FANN = 1

n

n∑

i=1

fANN,i ,

fEXP = Experimental, fANN = Predicted (5)

The methodology of constructing an optimum ANN architecture is as follows:

1. Start2. Normalization of data (inputs and outputs)3. Feeding the normalized data to the hidden layer of the ANN

A Review on the Fabrication of Surface Composites via Friction … 9

4. Determining the optimum values of the determinants involved5. Executing the training algorithm of the network6. Obtaining the Pearson coefficient7. If PCC is atleast 0.99, continue. If it is less than that, go back to the optimization

step (step 4)8. Till the convergence of experimental and predicted data is not obtained, the

processing is to be continued9. Weights and biases vectors are obtained10. Analysis is done based on the function used in the model11. Final error is calculated12. End.

4 Summary

Friction Stir Processing has a wide variety of applications which include, but arenot restricted to, surface modification and fabrication. There are numerous ways toincorporate reinforcement material into the surface of the base metal and these havebeen optimized over time. Friction Stir Processing can be used for achieving super-plasticity, producing alloys that possess special properties, improving the fatiguestrength of welded joints, etc. ANN has been used extensively and successfully tofigure out the most efficient parameter values for FSP by various researchers.

5 Future Scope

Friction Stir Processing has rapidly made a niche for itself in the industry. As it isa surface modification and fabrication technique that overrides the drawbacks of theexisting technologies by a mile, the acceptance of this technique has exponentiallygrown. Moreover, the fact that FSP can be implemented on existing CNC millingmachines, it is being adapted quickly far and wide through the industry. In additionto this, the varied applications of FSP, combined with its arsenal of advantages andeasy adaptability, have made FSP very promising. Since artificial neural networksare also increasingly being used all through the world, the ease of determination ofthe relationship between the various determinants and variables associated with FSPin a mathematical way, which can accurately predict the impact of every parameteron the resultant MMC, has not only drastically reduced the cost of experimentationand research but also made it far more accessible than before.

10 K. Bector et al.

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A Statistical Study of ConsumerPerspective Towards the Supply ChainManagement of Food Delivery Platforms

Gangesh Chawla, Keshav Aggarwal, N. Yuvraj, and Ranganath M. Singari

Abstract Supply Chain deals with fulfilling the customer request be it directly orindirectly. It involves forming a link between customers, warehouses, manufactures,retailers and suppliers. Customer experience is one factor that the company shouldkeep in mind when deciding its supply chain network. Smart Logistics, in recenttimes, has a huge role to play inmaking the supply chain efficient and responsive.Withthe combination of logistics and technology, the transparency in the whole processwould be increased which would lead to reduced turnaround time and ensuringsafety, quality and privacy of the items delivered. This research paper aims to analyzethe supply chain of food delivery business and based on those device strategieswhich ensure maximum participation of the customer at each stage in food deliverysupported by feedback from over 120 frequent users of food delivery business fromdifferent age brackets. The entire research happens in a step-by-stepmanner in whichthe first part is based on understanding the industry, finding the key parameters andthen collecting various survey opinions and responses to identify the issues facedduring food delivery and how it affects the entire supply chain and lastly making alist of the key considered challenges and prioritizing them using Decision-MakingTrial and Evaluation Laboratory (DEMATEL) method.

Keywords Supply chain · Turnaround time · Transparency · Smart logistics ·Minimum capital investment · New strategies · DEMATEL approach

G. Chawla (B) · K. AggarwalDepartment of Production and Industrial Engineering, Delhi Technological University, NewDelhi, Delhi 110042, Indiae-mail: [email protected]

N. YuvrajDepartment of Mechanical Engineering, Delhi Technological University, New Delhi, Delhi110042, India

R. M. SingariDepartment of Design, Delhi Technological University, New Delhi, Delhi 110042, India

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021R. M. Singari et al. (eds.), Advances in Manufacturing and Industrial Engineering,Lecture Notes in Mechanical Engineering,https://doi.org/10.1007/978-981-15-8542-5_2

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14 G. Chawla et al.

1 Introduction

India is unarguably one of the largest consumer marketplaces across the globe todaywith a population exceeding 1.3 billion. This young and new India’s appetite is oneof the major factors for the ever-increasing demand in the beverage and food industryon the whole [1]. Indian market is currently experiencing a boom in the food deliverysegment. Since this industry is on a rise, it is important to have an efficient supplychain to further facilitate expansion. Swiggy, till October 2018 had its operations innearly 28 cities across India. Now, it has covered a larger part of India with expandingits operations by introducing its services to 16 cities which were earlier not covered[2]. The global prospects are also very promising for this market. The total revenuein the online food delivery market totals up to US$7730 million in 2019 across theglobe as shown in Fig. 1 and by 2023 the total revenue of the online food deliverymarket is expected to touch the US$12,536 million [3].

Today, in the era of globalization the cost of any product is enormously impactedby the efficiency of the logistical setup applied during the various phases of its life.Smart logistics deals with smoothing all these phases as well as cutting the cost atthe same time. With the help of various technological setups in accordance withIndustry 4.0 principles [4], e.g., Physical Internet (based on the IoT), UAVs/Drones,Blockchain, Data Analytics, Robotics and Automation and Autonomous Vehicleswe can simplify most of the stages involved in the logistical setup (Fig. 2).

Fig. 1 Year-wise revenue comparison between restaurant to consumer delivery and platform toconsumer delivery in the world