Lecture Notes in Mechanical Engineering

V. C. PandeyP. M. PandeyS. K. Garg   Editors

Advances in Electromechanical TechnologiesSelect Proceedings of TEMT 2019

Lecture Notes in Mechanical Engineering

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Francisco Cavas-Martínez, Departamento de Estructuras, Universidad Politécnicade Cartagena, Cartagena, Murcia, Spain

Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia

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Vitalii Ivanov, Department of Manufacturing Engineering Machine and Tools,Sumy State University, Sumy, Ukraine

Young W. Kwon, Department of Manufacturing Engineering and AerospaceEngineering, Graduate School of Engineering and Applied Science, Monterey,CA, USA

Justyna Trojanowska, Poznan University of Technology, Poznan, Poland

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V. C. Pandey • P. M. Pandey • S. K. GargEditors

Advancesin ElectromechanicalTechnologiesSelect Proceedings of TEMT 2019

123

EditorsV. C. PandeyDepartment of Mechanicaland Automation EngineeringHMR Institute of Technologyand ManagementNew Delhi, Delhi, India

S. K. GargDepartment of Mechanical EngineeringDelhi Technical UniversityNew Delhi, Delhi, India

P. M. PandeyDepartment of Mechanical EngineeringIndian Institute of Technology DelhiNew Delhi, Delhi, India

ISSN 2195-4356 ISSN 2195-4364 (electronic)Lecture Notes in Mechanical EngineeringISBN 978-981-15-5462-9 ISBN 978-981-15-5463-6 (eBook)https://doi.org/10.1007/978-981-15-5463-6

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https://doi.org/10.1007/978-981-15-5463-6

Contents

Optimization of Energy-Aware Flexible Job Shop SchedulingProblem Using VNS-Based GA Approach . . . . . . . . . . . . . . . . . . . . . . 1Rakesh Kumar Phanden, Rahul Sindhwani, and Lalit Sharma

Optimizing the Conveyor Belt Speed of a Bright AnnealingFurnace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Akshay Naidu, R. Padmanaban, and R. Vaira Vignesh

FGM Plates with Circular Cut Out Analysis Resting on ElasticFoundations and in Thermomechanical Loading Environments . . . . . . 21Rajesh Kumar

Benchmarking the Integration of Industry 4.0 into the NationalPolicies at Asia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Sanjiv Narula, Surya Prakash, Maheshwar Dwivedy, Ajay Sood,and Vishal Talwar

Exergy Analysis of Novel Combined Absorption RefrigerationSystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Vaibhav Jain, Ashu Singhal, and Harsh Joshi

Geothermal Energy: An Effective Resource Toward Sustainability . . . 61Suman Das and Arijit Kundu

Analysis of Double Square Loop FSS for Transmission Mechanism . . . 73Rahul Shukla and Garima Tiwari

Development of High-Temperature Shape Memory Alloys . . . . . . . . . . 79Shyam Singh Rawat, Raghvendra Sharma, Maneeram Singh Gurjar,and Manoj Sharma

FGM Plates with Elliptical and Rectangular Cutouts Analysisfor Post-Buckling Resting on Elastic Foundations in ThermalEnvironments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Rajesh Kumar

v

Water Quality Examining Using Techniques of Data Mining . . . . . . . . 103Sanika Singh, Sudeshna Chakraborty, and Saurabh Mukherjee

Application of Block Chain in EHR’s System for Maintainingthe Privacy of Patients Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Ifra Salaudin, Shri Kant, and Supriya Khaitan

Solar Power-Based Smart Greenhouse . . . . . . . . . . . . . . . . . . . . . . . . . 127Padma Wangmo, Vinay Kumar Jadoun, Anshul Agarwal,and Harish Kumar

Modelling of Slag Produced in Submerged Arc Welding . . . . . . . . . . . 137Brijpal Singh and Sachin Dhull

Automatic Land Defense System for Borders Using Radar, LaserGun and Related Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145Rishab Kumar Bhardwaj, Sudhir Ranwa, Ranjan Kumar, Lokesh Meena,Anshul Agarwal, and Vinay Kumar Jadoun

A Wideband Star-Shaped Rectenna for RF Energy Harvestingin GSM Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163Vishal Singh, Vinay Shankar Pandey, and Vivek Shrivastava

Cylindrical Shell Panel of FGM Analysis Elastically Supportedand Uncertain Material Parameters in HygrothermomechanicalLoading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175Rajesh Kumar

Influence of Digital Technologies on Migration Flowsand the Regional Labor Market of Russia . . . . . . . . . . . . . . . . . . . . . . 185Kruglov Dmitrii, Tsygankova Inga, Reznikova Olga,and Mikhailov Sergey

Analysis of Actuators Prognostic Health Monitoring in SpacecraftAttitude Control Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195Kaustav Jyoti Borah

Modified Mechanical Face Seal Geometry . . . . . . . . . . . . . . . . . . . . . . 205Ankita Kumari

Design and Analysis of a CSP Plant Integrated with PCM Reservoirsin a Combined Storage System for Uninterrupted PowerProduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213Bikash Banerjee and Asim Mahapatra

Application of Analytic Hierarchy Process for the Selection of BestTablet Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227Shankha Shubhra Goswami and Soupayan Mitra

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Postdeforming of FGM Laminates Analysis for Random GeometricParameters in Thermal Environments . . . . . . . . . . . . . . . . . . . . . . . . . 237Rajesh Kumar and Vineet Shekher

Flow Simulation of Atmospheric Re-entry Vehicle at Varying MachNumber and Angle of Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245Shivam Thakur, Harish Kumar, and Shrutidhara Sarma

Measuring the Relative Importance of ReconfigurableManufacturing System (RMS) Using Best–Worst Method (BWM) . . . . 253Ashutosh Singh, Mohammad Asjad, Piyush Gupta, Zahid Akhtar Khan,and Arshad Noor Siddiquee

E-Commerce Delivery Routing System Using Bellman–Held–KarpAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277Sugandh Agarwal, Naman Jain, Tanupriya Choudhury,Utkarsh Vikram Singh, and Ravi Tomar

GAPER: Gender, Age, Pose and Emotion Recognition Using DeepNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287Deepali Virmani, Tanu Sharma, and Muskan Garg

Enhanced Blockchain Application for Pub-Sub Model . . . . . . . . . . . . . 299Nomaun Rathore and Shri Kant

A Study on Perception of Management Students RegardingCorporate Governance Practices of PSUs . . . . . . . . . . . . . . . . . . . . . . . 313Meenakshi Bisla, Pranav Mishra, Aparna Sharma, and Priyanka Tyagi

Discrimination of Text and Non-text Images . . . . . . . . . . . . . . . . . . . . 323Pradipta Karmakar, Chowdhury MdMizan, Rani Astya,and Sudeshna Chakraborty

Calibration of Temperature and Pressure Sensors for DAQ Systemin Air Conditioning Test Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333Vrinatri Velentina Boro, Vibha Burman, Amandeep Kaur, Manoj Soni,and Pooja Bhati

On Condition Monitoring Aspects of in-Service Power TransformersUsing Computational Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343Ujjawal Prakash Bhushan, R. K. Jarial, Vinay Kumar Jadoun,and Anshul Agarwal

Communication Techniques in Smart Grid—A State of Art . . . . . . . . 357Aastha Khanna and Anuradha Tomar

FGM Composite Cylindrical Shell Panel Analysis for Post BucklingResting on Elastic Foundations and Thermomechanical Loading . . . . . 377Rajesh Kumar

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PV-Based Water Pumping System—A Comprehensive Review . . . . . . 389Sahil Sharma, Anuradha Tomar, and Vishesh Bhagat

Economic Analysis of Battery Swap Station for Electric ThreeWheeled Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399Devanshu Grover, Ishan, Shubham Bansal, and R. C. Saini

Optimization of Inlet Swirl for Flow Separation in AnnularDiffuser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409Hardial Singh and B. B. Arora

Innovative PMI-Based Inspection Planning for Planarand Cylindrical Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421Pratik Kalaskar, Surbhi Razdan, and Amol Jawalkar

Experimental Study of Sliding Wear Behavior of Journal BearingMaterials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429Vinayak Goel, Akshat Jain, Vibhor Heta, Sanchit Jain,and Vipin Kumar Sharma

Deep Learning Architectures: A Hierarchy in Convolution NeuralNetwork Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Shruti Karkra, Priti Singh, Karamjit Kaur, and Rohan Sharma

A Comprehensive Study of Different Converter Topologiesfor Photovoltaic System Under Variable EnvironmentalConditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459Preeti Gupta and S. L. Shimi

A Novel Approach for Predicting the Compressive and FlexuralStrength of Steel Slag Mixed Concrete Using Feed-ForwardNeural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475Tanvi Gupta and S. N. Sachdeva

Progress and Latest Developments in Hybrid Solar Dryingwith Thermal Energy Storage System . . . . . . . . . . . . . . . . . . . . . . . . . 487Narender Sinhmar and Pushpendra Singh

An Improved Maximum Power Point Tracking (MPPT)of a Partially Shaded Solar PV System Using PSO with ConstrictionFactor (PSO-CF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499Imran Pervez, Adil Sarwar, Arsalan Pervez, Mohd Tariq,and Mohammad Zaid

Maximum Power Point Tracking of a Partially Shaded Solar PVGeneration System Using Coyote Optimization Algorithm (COA) . . . . 509Imran Pervez, Adil Sarwar, Arsalan Pervez, Mohd Tariq,and Mohammad Zaid

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Design and Simulation of Front-End Converter Based PowerConditioning Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519Deepak Upadhyay, Shahbaz Ahmad Khan, and Mohd Tariq

Enhancing Mechanical Properties via Semi-solid Metal Processingof A356 Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531Nishant Bhasin, Harkrit Chhatwal, Aditya Bassi, Rahul Sarma,Sumit Sharma, and Vipin Kaushik

Comparative Analysis of Different Materials for Cylinderand Justification by Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539Ujjwal Singh, Jatin Lingwal, Nirmal Chakraborty, Ankit Kumar,Sumit Sharma, and Vipin Kuashik

Performance of an Outdoor Optical Wireless CommunicationChannel Through Gamma-Gamma Turbulence at DifferentFrequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551Nitin Garg and Anwar Ahmad

Ergodic Capacity Analysis of Optical Wireless Communication LinksOver M-Atmospheric Turbulence Channel with Pointing LossesGiven by Beckmann Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561Nitin Garg and Anwar Ahmad

Effect of Impurities on Charging Track in the Performanceof Wireless Capacitive Charging Technique . . . . . . . . . . . . . . . . . . . . . 573Mohd Shahvez, Sameer Pervez Shamsi, and Mohd Tariq

“RESUME SELECTOR” Using Pyspark and Hadoop . . . . . . . . . . . . . 585Preeti Arora, Deepali Virmani, Aradhay Jain, and Akshay Vats

Implementation of Regenerative Braking System in Automobiles . . . . . 595Mohan Kumar and Md. Ehsan Asgar

Regression Approach to Power Transformer Health AssessmentUsing Health Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603Jagdish Prasad Sharma

Functional Link Neural Network-Based Prediction of Compressiveand Flexural Strengths of Jarosite Mixed Cement ConcretePavements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617Tanvi Gupta and S. N. Sachdeva

A Newer Universal Model for Attaining Thin Film of VariedComposition During Sputtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629Gaurav Gupta and R. K. Tyagi

A Novel Model for IoT-Based Meter Using ATmega328PMicrocontroller and Google Cloud Store . . . . . . . . . . . . . . . . . . . . . . . 639Sufia Khalid, Mohammad Sarfraz, Vishal Singh, Aafreen, and Ali Allahloh

Contents ix

Vibration Analysis of Curved Beam Using Higher Order ShearDeformation Theory with Different Boundary Conditions . . . . . . . . . . 649Md. Rashid Akhtar and Aas Mohammad

Performance Analysis of Alternate Purification System in AirConditioning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661Ashish Gangal and Vaibhav Jain

Weed Detection Approach Using Feature Extraction and KNNClassification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671Gurpreet Khurana and Navneet Kaur Bawa

Analysis of Transition Metal Dichalcogenide MaterialsBased Nanotube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681Prateek Kumar, Maneesha Gupta, and Kunwar Singh

Automated CNC Programming by the Restricted BoltzmannMachine Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691Neelima and Vivek Chawla

Single Pass Wavy Channel Heat Exchanger for ThermalManagement of Electric Vehicle Battery Pack—A NumericalStudy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711Babu Rao Ponangi, Pramath H. Srikanth, and Pratyush V. Heblikar

Comparative Microstructural Investigation of Aluminium SiliconCarbide–Mg and Aluminium Boron Carbide–Mg Particulate MetalMatrix Composite Fabricated by Stir Casting . . . . . . . . . . . . . . . . . . . 725Paridhi Malhotra, R. K. Tyagi, Nishant K. Singh,and Basant Singh Sikarwar

A Review on IoT-Based Hybrid Navigation System for Mid-sizedAutonomous Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735Ajay K. S. Singholi, Mamta Mittal, and Ankur Bhargava

Development and Modelling of a Novel Wheelchair with StaircaseClimbing Ability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745Gaurav Kesari

Design and Fabrication of Low-Cost Detachable Power Unitfor a Wheelchair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755Issac Thomas, M. I. John, Robinson Lal, Jobi Lukose, and J. Sanjog

Computation of Rupture Strain from Macroscopic Criteria . . . . . . . . . 765Appurva Jain and Abhishek Mishra

Biosignal Analysis Using Independent Componentswith Intelligent Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 771Suhani Pandey and Mohammad Sarfraz

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Modeling of Multiple Jointed Kinematic Chains Usingthe Polynomial Coefficients Derived from the InteractiveWeighted Matrices of Kinematic Graphs . . . . . . . . . . . . . . . . . . . . . . . 787Vipin Kaushik and Aas Mohammad

Application of Wavelet Analysis in Condition Monitoringof Induction Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795Amandeep Sharma, Pankaj Verma, Anurag Choudhary, Lini Mathew,and Shantanu Chatterji

Optimization of Halon 1301 Discharge Through Fire ExtinguisherCylinder for IFSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809Reetik Kaushik, Yasham Raj Jaiswal, Vishal Dwivedi,and Ranganath M. Singari

Barriers to the Use of Robots in Indian Industries . . . . . . . . . . . . . . . . 821Ravindra Kumar

Impacts of Regenerative Braking on Li-Ion Battery . . . . . . . . . . . . . . . 831Akshay Thakur, Kaleem Uz Zaman Khan, Jatin Gupta, Kunal Gupta,Mukund Vats, Chetan Mishra, and Aditya Roy

Development of the Latent Heat Storage System Using PhaseChange Material with Insertion of Helical Fins to Improve HeatTransfer Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843Vishal Godase, Ashok Pise, and Avinash Waghmare

Experimental Investigation of Helical Coil Tube in Tube HeatExchanger with Microfins Using Al2O3/Water Nano Fluid . . . . . . . . . . 855Nilesh K. Kadam and A. R. Acharya

Analysis of Vapour Compression Refrigeration System in Termsof Convective Heat Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 873Pinaki Das, Dheeraj Chhabra, Mukul Krishnatrey, and Mayur

Microwave Welding of Mild Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 885Sourav, Uma Gautam, Akshay Marwah, Ankit Sharma, and Lakshay

Synthesis of Nanocellulose Fibrils/Particles from Cellulose FibresThrough Sporadic Homogenization . . . . . . . . . . . . . . . . . . . . . . . . . . . 893Nadendla Srinivasababu and Kopparthi Phaneendra Kumar

A New Design of Li-Ion Battery for a Smart Suitcase . . . . . . . . . . . . . 903Anant Singhal, Karan Bhatia, Kaleem Uz Zaman Khan, Shivam Tyagi,Tarun Mittal, Chetan Mishra, and Aditya Roy

Comparative Study on the Formability Behaviour of DifferentGrades of Aluminium Alloys Using Limiting Dome Height Test—AnAnalytical and Experimental Approach . . . . . . . . . . . . . . . . . . . . . . . . 915Praveen Kumar and Satpal Sharma

Contents xi

A Novel Approach of Gearbox Fault Diagnosis by Using TimeSynchronous Averaging and J48 Algorithm . . . . . . . . . . . . . . . . . . . . . 927Subrata Mukherjee, Rishubh Kaushal, Vikash Kumar,and Somnath Sarangi

Programmable Logic Controller Controlled 360 Degree FlexibleDrilling Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937Parth Patpatiya, Varun Bhatnagar, Harshita Tyagi, and Nupur Anand

“VISIO”: An IoT Device for Assistance of Visually Challenged . . . . . . 949Rashbir Singh, Prateek Singh, Deepak Chahal, and Latika Kharb

A Retrospective Investigation of Mechanical and Physical Propertiesof ABS Specimen Developed by Manual Injection Mouldingand Fused Deposition Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 965Md Qamar Tanveer, Mohd Suhaib, and Abid Haleem

Effect of Magnetic Pole Orientation on Viscoelastic MagneticAbrasive Finishing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 979K. Srinivas, Q. Murtaza, and A. K. Aggarwal

MHO Shape Slot Microstrip Patch Antenna for X-Band . . . . . . . . . . . 989Palak Jain and Sunil Kumar Singh

Optimization of Process Parameters in Electric Discharge Machiningfor SS420 Using Taguchi Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . 997Sudhir Kumar, Sanjoy Kumar Ghoshal, and Pawan Kumar Arora

Improvement in Starting Characteristics of a HermeticReciprocating Compressor by Offset CylinderArrangement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005Himanshu Kalbandhe, Anil Acharya, and Sumedh Nalavade

Stair Shape Microstrip Patch Antenna for X Band . . . . . . . . . . . . . . . 1017Nivedita Dash and Sunil Kumar Singh

Power Consumption Estimation of SHA-3 for the Internetof Things Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025M. Tariq Banday and Issmat Shah Masoodi

Joining of Metals Using Microwave Energy . . . . . . . . . . . . . . . . . . . . . 1035Uma Gautam and Vipin

xii Contents

About the Editors

Dr. V. C. Pandey is Director and Professor in HMRITM, Delhi. He obtained hisPh.D. from Delhi College of Engineering (Delhi University) in 2011. He completedhis M.E. in Industrial Engineering & Management in 1996 from SGSITS, DAVVIndore and B.E. in Mechanical Engineering from MMMEC Gorakhpur in 1994. Hehas more than 24 years rich experience of industry, research, teaching andadministration in the organizations of repute in Bangalore and Delhi NCR. Heworked as the head of the institution in three different engineering colleges in NCR.He has quality seven research publications in his credit. He guided more than30 projects at undergraduate level. He attended more than twenty five workshopsand conferences in best institutions of the country. His area of interest is AdvancedManufacturing Systems, Lean and Agile Manufacturing, Operation Managementand Supply Chain Management. He is actively involved as the reviewer in the bestjournals of his research area. He is life member of various professional bodies likeISTE, SAE and IIIE.

Dr. P. M. Pandey is currently working as Professor in IIT Delhi. He completed hisB.Tech. from H.B.T.I. Kanpur in 1993 securing first position and got Master’sdegree from IIT Kanpur in 1995 in Manufacturing Science specialization, heobtained his Ph.D. in the area of Additive Manufacturing/3D Printing from IITKanpur in 2003. Dr. Pandey diversified his research areas in the field of micro andnano finishing, micro-deposition and also continued working in the area of 3DPrinting. He supervised 25 PhDs and more than 33 MTech theses in last 10 yearsand also filed 16 Indian patent applications. He has approximately 137 internationaljournal papers and 44 international/national refereed conference papers to his credit.He received Highly Commended Paper Award by Rapid Prototyping Journal for thepaper “Fabrication of three dimensional open porous regular structure of PA 2200for enhanced strength of scaffold using selective laser sintering” published in 2017.He is recipient of Outstanding Young Faculty Fellowship (IIT Delhi) andJ.M. Mahajan outstanding teacher award of IIT Delhi.

xiii

Dr. S. K. Garg is Pro Vice Chancellor and Professor in Delhi TechnologicalUniversity (Formerly Delhi College of Engineering). He was appointed asIndependent Director to a Navratna Public Sector Enterprise by GOI for a period ofthree years from 2012-2015. He is a recipient of Dewang Mehta National EducationAward for Best Professor in Operations and manufacturing in the year 2015.Prof. Garg has more than 28 years of experience in industry, teaching and research.His teaching and research areas include supply chain management, manufacturingprocess automation and technology management, operations management, materi-als management, operations research, manufacturing strategy, production planningand control etc. He has guided 17 PhDs, 70 M.Tech thesis and 50 B.Tech projects.He has published 175 papers including 75 in international journals. Prof. Garg ismember of the editorial boards of several international and national journals. He isreviewer of research papers of international journals, conferences and also examinerto Ph.D. and M.Tech thesis of different universities.

xiv About the Editors

Optimization of Energy-Aware FlexibleJob Shop Scheduling Problem UsingVNS-Based GA Approach

Rakesh Kumar Phanden, Rahul Sindhwani, and Lalit Sharma

Abstract In today’s world, the manufacturing systems are growing day by day andcapable to produce the products on time as per the customers’ requirement. However,the energy consumption by these manufacturing systems has been ignored, and ahigher amount of energy is consuming to increase the production rate. Therefore, itis must to consider the criteria of energy consumption along with other traditionalobjectives of performance measures. Thus, in the present work, energy consumptionhas been considered with other measures to solve the flexible job shop scheduling(JSS) problem. It is a non-polynomial (NP) hard problem, and this problem belongsto the class of combinatorial optimization, so it is difficult to solve with a simpleand exact mathematical formulation. Thus, this article presents the modified geneticalgorithm (GA)-based methodology to deal with flexible JSS problem. The GA hasbeen modified in order to increase local search using variable neighbourhood search(VNS)-based mutation operator in order to avoid premature convergence of regularGA. The proposed approach considers multiple objectives in order to produce anoptimized solution for flexible JSSproblemsuch asmakespan, processing cost aswellas the energy consumption. In present work, an alternative (flexible) manufacturingprocess has been considered to extend the JSS problem. A suitable chromosomehas been designed to code the schedule (solution) for JSS problem having additionalprocessing flexibility. A case study (of 6 jobs and 15machines) has been presented inorder to assess the effectiveness of projectedmodifiedGAmethod. Results reveal thatthe proposed VNS-based approach in GA is effective enough to reduce makespan,processing cost as well as energy consumption performance measures.

Keywords Flexible job shop scheduling · Variable neighbourhood search ·Energy-aware optimization · Modified genetic algorithm

R. K. Phanden · R. Sindhwani · L. Sharma (B)Department of Mechanical Engineering, Amity School of Engineering and Technology, AmityUniversity, Noida, Uttar Pradesh 201313, Indiae-mail: lalitsharma31296@gmail.com

© Springer Nature Singapore Pte Ltd. 2021V. C. Pandey et al. (eds.), Advances in Electromechanical Technologies,Lecture Notes in Mechanical Engineering,https://doi.org/10.1007/978-981-15-5463-6_1

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2 R. K. Phanden et al.

1 Introduction

Since environmental awareness is increasing, the energy-efficient consumption factoramong the other production functions has been identified as an important objectiveto be considered during the optimization of a manufacturing system. For the start ofthe new industrial revolution, the industrial sector comes with a higher amount ofenergy consumption to increase the production rate. Almost 33% of total energy hasbeen consumed by production units, and they contribute 38% in greenhouse emission[1]. Furthermore, in order to satisfy the requirement of sustainable development, theproduction industries are facing newchallenges,which help to achieve the ecological,social and economic aims. So, it is essential for industrial sectors that the manufac-turing community has to access the systems which can increase the productivity ofa production unit by decreasing its energy eating by incorporating latest technologycapability as well as efficient planning and scheduling methods. Thus, in the presentwork, an approach has been presented to optimize the energy consumption with otherperformance-measuring criterion. The flexible job shop manufacturing environmenthas been considered to generate an optimized schedule. Multiple objectives havebeen taken into consideration, and a modified genetic algorithm (GA) approach hasbeen applied to optimize the taken objective functions.

Production scheduling concerns with allotment process of operations of all jobs tomachines that depend on availability of machine tools as well as the time constraintsof processes. Production scheduling becomes energy efficient when one undertakesthe environmental impacts like energy consumption and carbon emissions duringits optimization along with other traditional objective functions. Thus, the investi-gations to curtail consumption of energy in the production unit while productionscheduling has been steadily booming [2]. Some of the acknowledged researchstudies considering the impact of energy consumption during the scheduling arediscussed below.

A model containing multiple objectives to reduce the consumption of energy aswell as the total completion time while exploring the scheduling of jobs for a CNCmachine has been proposed by Mouzon et al. [1]. Scheduling algorithms evolvinginteger programming models have been proposed for flow shop manufacturing envi-ronment in order to control the carbon footprints and power consumption withoptimized makespan performance measure [2–4]. Also, the flow shop schedulingproblems were solved to reduce time, makespan as well as energy consumptionusing branch and bound algorithms and NSGA-II [2, 5]. A mathematical modelwas presented to optimize energy consumption in flexible manufacturing system [6].Also, the job shop scheduling (JSS) problem has been solved along with a mixedinteger programming model for energy consumption criterion including makespan,robustness, noise reduction, processing cost and other performance measures usingIBM ILOG CPLEX, simplex lattice design-based GA and whale algorithm of opti-mization [7–11]. The relationship between energy efficient,makespan and robustnesshas been identified, and disturbance has been studied during energy-aware produc-tion scheduling in a job shop [7]. It can be perceived from literature that, however,

Optimization of Energy-Aware Flexible Job Shop Scheduling … 3

the GA has been comprehensively applied to solve the taken problem; it is unreal-istic to state that the application of an individual algorithm is superior to other sincethey have heuristic way of working. It can easily be stated that the research on greenproduction scheduling is in primeval phase. Most of research articles possessed verysimple cases such as limited number of parts to be process on a machine and flowshops scheduling with limited number of connected machines. Another importantconcern is regarding the problem to evaluate the active status of resources (mainlymachines) and its corresponding environmental effects with operations. Practically,only three statuses have been considered and explored which are “on”, “off” and“idle”. Theoretically, the “standby” status is also found in literature [8]. Moreover,the flexible JSS problem has been solved using a modified biogeography-based opti-mization algorithm integrated with the variable neighbourhood search algorithm,grey wolf optimization algorithm with double-searching mode, memetic algorithm,bi-population-based discrete cat swarm optimization algorithm and particle swarmoptimization algorithm to optimizemakespan, electricity consumption cost and tardi-ness performance measures [12–15]. Thus, the literature review clearly reveals thatthe genetic algorithm with neighbourhood search technique has not been applied tooptimize the flexible JSS for multi-objectives of minimizing energy consumption,processing cost and makespan. Rest of the sections of this article presents a litera-ture review on the energy-aware contributions for production scheduling problems,problem formulation and mathematical model. Also, the adopted methodology hasbeen elaborated before results and discussion. In the end, conclusions have beendrawn from the present study.

2 Problem Statement

The flexible job shop problem can be stated as “a group of ‘n’ number of jobs (inwhich the job ‘J’ varies from 1 to ‘n’ numbers) are processed on a group of ‘m’number of machines (in which the machine ‘M’ varies from 1 to ‘m’) and each jobis characterized by a set of operations ‘O’ and set of alternative (flexible) processplan available to process”. The schedule is determined based on different constraintsbetween operations. Operation of a job in various alternative process plans can beprocessed on diverse machines with varying level of energy consumption or on asimilar machine having diverse processing parameters. Consequently, the operationof each job “j” on machine “m” has operation time and equivalent energy consump-tion. Thus, a problem is formalized to assigns the jobs to the machines as well to findthe sequence of operations on each machine for optimal solution of processing cost,makespan and energy consumption with equal weighting. It follows the followingassumptions and constraints: (a) all machines, as well as jobs, are always ready tostart by zero-time unit, (b) more than one operation is not allowed on a machine, (c)part pre-emption is not allowed, (d) precedence is applied between operations of ajob, and (e) each job must follow the sequence of operation on machines.

4 R. K. Phanden et al.

3 Mathematical Model

This section presents the energy-aware modelling for the production schedulingproblem that occurs in a job shop floor considering flexible (alternative) processplans. The optimization of energy consumption, processing cost and makespan forflexible JSS problem is solved by a mathematical model (mixed integer program-ming) suggested by Dai et al. [16, 17]. In addition, the present work considers theminimization of processing cost as another function with the functions proposed byDai et al. [16, 17]. Thus, three objectives of optimization, viz. energy consumption,makespan as well as processing cost have been taken to minimize for flexible JSSproblem. The model is presented using the notations from Dai et al. [16, 17].

Themodel for consumption of energy has been established on basis of the existingresearch on energy-efficient manufacturing process [16, 17]. The total consumptionof energy in the production process can be distributed into three categories—(a) basicconsumption of energy: basic energy is the energy utilized to control the regularoperation of a machine parts; it includes power consumption of the motor drivescomponents, and servo feed drive parts, main spindle drive parts, supporting partsused for lubrication, cooling, hydraulic mechanisms and control devices as wellas any other periphery parts; (b) consumption of energy for unloading activities:consumptions of energy for unloading like loading, unloading, positioning, fasteningof workpiece and the exchanging of cutting tool and tool bit holders; (c) consumptionof energy for cutting operation: consumption of energy for cutting is the actual cuttingoperation. This article considers the energy-efficient manufacturing processes; theprimary contributors to the total energy consumption are the consumption of energyfor unloading and the cutting. Thus, by considering the aforementioned assumption,the objective is tominimize the total consumption of energy (it is consisting of direct-energy use up to remove material during the productive state and indirect-energyconsumed in unfruitful states) [17].

Minimization of Energy Consumption

=∑

k∈Ol j

∑

l∈G j

∑

j∈J

∑

i∈Pm

∑

m∈M

((α · (Pcmkl j )2 + β · Pcmkl j + Pumkl j

) · T mkl j · Xl j · Y imkl j)

∑

k,q∈Ol j,r p

∑

l,r∈G j,p

∑

j,p∈J

∑

i∈Pm

∑

m∈M

(Pumkl j ·

((Cimkl j − T mkl j

) · Xi j · Y imkl j − C (i−1)mqrp

·Xrp · Y (i−1)mqrp) · Xl j · Xrp · Zmkl jqrp

)

Here, in the right-hand side, the first equation is representing the direct-energyconsumed by removal materials in the productive state; the coefficients of the loadconsumption of energy are represented by α, β that can be found from the linearregression equations as per the idle consumption of energy with varying levels ofspindle speed. In the right-hand side, the second equation is representing indirectconsumption of energy, like energy for backup [17].

Optimization of Energy-Aware Flexible Job Shop Scheduling … 5

Minimization of Processing Cost =∑

j∈J

∑

k∈Ol jPmmjk T

mkl j C Pm+

∑

j∈JC R j

Minimization of Makespan = maxj∈J

(Cimkl j Xl j Y

imkl j

)

4 Adopted Methodology

There is a vast solution search space while optimizing the energy-aware modelof scheduling problem of a job shop configuration having availability of differentmachines to process a given job mix. In the present work, a multi-objective functionwith constraints and mixed integer programming has opted from Dai et al. [16, 17].It is required to find an optimal (or near-optimal) results on the basis of an intelligentalgorithm to ease the optimization process as per the defined production schedulingcriteria. Therefore, in this study, a modified GA that combines a GA with a VNSis adapted in order to find an optimal result of the objective’s functions given inprevious section.

Genetic algorithm is the nature inspired search method that works on the basisof the process of natural biological evolution [18]. GA has been extensively used tosolve combinatorial optimization problems. The main advantage of GA is to get agood results quickly and efficiently for an objective function in a complex solutionspace. However, a major disadvantage of GA is that it possibly being stuck in alocal optimum solution; this phenomenon is termed as premature convergence ofGA. Therefore, the VNS has been planned to perform the mutation operation of GA.VNS is a local searchingwhich emphasize on increasing the fitness of current solutionthrough a procedure and continually changing the structures of the neighbourhood(solution) during the evolution of local search of solution space. VNS has beensuccessfully applied to combinational complex problems [19]. The main advantageofVNSalgorithm is that it can easily avoid the local convergence duringoptimization.Also, it can efficiently explore a difficult solution space in order to find the globaloptimum value of objective. Thus, in this paper, an attempt has beenmade to increasethe strength of GA by incorporating the VNS. Figure 1 demonstrates the flowchartof VNS-based modified GA for the flexible JSS problem.

4.1 Chromosome Structure (Encoding)

The present work utilizes multi-layered chromosome representation for the selectionof alternative process plan and correspondingproduction schedule. Startingportionofthe chromosome is reserved for process plan selection inwhich each gene represents aprocess plan number for each job. For example, if there are three jobs in the production

6 R. K. Phanden et al.

Fig. 1 VNS-based modifiedGA

Optimization of Energy-Aware Flexible Job Shop Scheduling … 7

order, the first position (gene) represents the process plan number of job-1, the secondposition is for process plan number of the job-2, and third position is for process plannumber of job-3. Hence, the length of the first layer is equal to the number of jobs inthe production order. Next portion of the chromosome is utilizing operation-basedrepresentation of production schedule, in which a total number of operations of theproduction order is coded. Each gene represents the operation number of each job,i.e. the length of this portion is equal to the number of operations in production order.

4.2 Generation of Initial Population

A feasible set of initial population is generated as follows; (i) set the length of an alter-native process plan string equal to the total number of jobs in production order. (ii) Thenumber of operations

(ol j

(l ∈ G j , j ∈ J

))of job j that containsmaximumoperations

between g j ( j ∈ J ) alternative process plans is precise as max(ol j

). Thus, the gene

thread (of scheduling plan) have the total length in equal to the sum of the maximumlength of each job, i.e. expressed as

∑nj=1 maxl

(ol j

). Thus, n + ∑nj=1 maxl

(ol j

)is

the total length of the chromosome. In case, the length of chosen gene string (ofprocess plan) of job j does not match with the maximum number

(maxl

(ol j

))during

decoding procedure, the components of operations of job j are detached from the lastoperation position to the first until unless the length is fulfilled through taken point.For example, if the size of the part mix is three and each part has four operations, thenthe chromosome embraces 15 numbers of total genes. In this study, three objectiveshave been considered to find a set of efficient results in a solution space, i.e. mini-mization of makespan and minimization of energy consumption and minimizationof processing cost for the taken job shop environment.

4.3 GA Operations

AregularGAworkswith three kinds of genetic operators, namely selection, crossoverand mutation in a series. Each operator is equally important to achieve the optimizedsolution. Selection operator chooses the elite solutions from the population and trans-fers it for crossover and mutation. Crossover generates fresh individuals embracingparental information andmutation produce fresh offspring holding fresh information.

(a) Selection. The present study considers linear-ranking selection along withstochastic universal sampling scheme. Here, chromosomes are arranged indecreasing order with respect to the absolute fitness values, and expected valuesare assigned to the ordered population as per their rank. The stochastic samplingscheme is used to choose the parents, and mating pool of selected chromosomesis created [20–26].

(b) Crossover. The method used for crossover operation is presented in Table 1.

8 R. K. Phanden et al.

Table 1 Steps of crossover operation in modified GA

Steps Description

1 Select two parent strings randomly from the population

2 Generate two blank chromosomes

3 Form the alternative process plan individuals in two blanks generated in Step 2

3.1 Pick a crossover position for a couple of alternative process plan individual, randomly.Also, the left portion and right portion from picked point termed as first and secondsections of crossover, respectively

3.2 In the first section of the alternative process plan of the individual (from two parentstrings), the bit should be transferred as per the crossover position into the same point asthe two blank chromosomes

3.3 In the second section of alternative process plan of the individual (from two parentstrings), the bit should be transferred as per the crossover position into the same point asthe two blank chromosomes, reverse to the first section

4 Form the schedule individuals in two blanks generated in Step 2. Note: repeat the sameprocedure for schedule string as explained in Step 3.1–3.3 for alternative process planindividual’s crossover

5 Update the current population with the individuals undergoes crossover operation

(c) VNS-basedMutation. Mutation operator is used after the crossover operator inorder to produce schedules containing fresh information. In mutation operator,there are numerous approaches for instance immunity-based mutation opera-tors and uniform/non-uniform mutation which are utilized to resolve intricate(global) problems of optimization. In the present work, a VNS-based mutationprocedure is considered to fit in the working of regular GA. In VNS, neighbour-hood structure evaluation method acts with the important character to guideand control the performance of algorithm. Moreover, it has been concluded byvarious researchers that the local optimal solutions in diverse neighbourhoodsare essentially undeviating. Also, a globally optimal solution is a locally optimalsolution corresponding to the entire vicinity, and the local optimal solutions forvarious neighbourhood structures are quite near to each other. The idea behindthe success ofVNSoptimization for local search is the changing capability of theneighbourhood structures methodically to improve the current optimized globalsolution. In the present work, TEN—“two-point exchange neighbourhood” andRIN—“random insert neighbourhood” structure have been adopted for evalua-tion. Both are simple neighbourhood structures. TEN exchanges two operationsin the encoding sequence randomly. RIN method is randomly selecting oneelement from the particle and inserting it to another position of the encodingsequence. Hence, the VNS-based mutation operator can improve the searcha-bility and the search efficiency of the algorithm by using two-point exchangeneighbourhood structure [19]. In this study, two types of neighbourhood struc-tures (TEN and RIN) are considered. N1 is selected as a principal neighbour-hood (TEN), and N2 is set as a subordinate neighbourhood (RIN) in VNS-based

Optimization of Energy-Aware Flexible Job Shop Scheduling … 9

Table 2 Steps of VNS-based mutation operation in modified GA

Steps Description

1 Retrieve initial population (x) from GA, after crossover

2 Create the sequence of neighbourhood structures (Sq), where, q = 1, 2, 3…, qmax3 Start from q = 1 and follow the steps from 3.1 to 3.3 till q = qmax3.1 Shaking: Create a neighbourhood solution (x0) from qth neighbourhood (Sq) of (x),

randomly.

3.2 Local Search: Find best neighbour (x00) of (x′) in the current neighbourhood (Sq)3.3 Move or Not?

If x00 is better than x,let x = x00 and q = 1,Restart local search of N1;otherwise,set q = q + 1

mutation operation. The steps of VNS-based mutation operation are presentedin Table 2.

5 Results and Discussion

The MATLAB® programming tool is used to implement the modified GA approachfor flexible JSS problem. The personal computer is used to conduct the test withthe configuration of Intel Pentium® 4 GB memory, 3.20 GHz processor speed andWindows 10 operating system.

The proposed approach has been texted for a case study from Phanden and Jain[23]. The power (idle) consumptions for machines are taken from a job shop instancedeveloped by Liu et al. [27]. It has been considered that all machines under exam-ination are the mechanized and same value of cutting power has been set for eachmachine.

A production order containing fifteen machines and six jobs has been taken fromPhanden and Jain [23]. Figure 2 indicates Gantt chart of the optimized schedule.The value of optimized makespan was 183 units using the proposed approach.Table 3 presents the comparison of proposed VNS-based GA approach with theregular GA (without VNS-based mutation operator). Results show that the proposedapproach is effective enough to optimize the multi-objectives. Results revealed thatthe makespan has been improved by 10.29%. Also, the value processing cost andenergy consumption values have been optimized by 3.86% and 7.55%, respectively.

The percentage improvement is computed as follows.

% age improvement = (value of PM byRGA − value of PM by VGA)/(value of PM by RGA) × 100

10 R. K. Phanden et al.

M15

M14

M13

M12

M11

M10

M9

M8

M7

M6

M5

M4

M3

M2

M1 J2 O3

J3 O3

J6O3

J1 O4

J5O1

J6 O1

J3 O2

J4 O2

J2 O4

J6 O4

J1 O5

J5 O2 J1 O3

J1 O2

J4 O1

J1 O1

J2 O1

J3 O1

J6 O2

J2 O2

J6 O5

J1 O6

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190

Fig. 2 Gantt chart of optimized schedule

Table 3 Comparison of theproposed approach withregular GA

Performancemeasures (PM)

Regular GA(RGA)

Proposedapproach(VGA)

%ageimprovement

Makespan 204 183 10.29

Processing cost 518 498 3.86

Energyconsumption

887 820 7.55

Thus, it can be safely concluded that formalized VNS-based GA approach isbetter than the conventional regular GA approach for makespan, processing cost andenergy consumption.

6 Conclusion

The present article addresses the issue of environmental awareness in terms of opti-mizing energy consumption along with other conventional performance measuresduring the scheduling for a flexible job shop manufacturing environment. Therefore,a modified GA-based approach to solve flexible JSS problem considering multi-objectives such as makespan, processing cost and energy consumption has beenproposed. A mutation operator of regular GA has been modified with the applicationofVNS algorithm. TEN—“two-point exchange neighbourhood” andRIN—“randominsert neighbourhood” structures have been successfully applied in place of mutationoperation during regular GA. Results reveal that proposed approach outperformedregular GA. The proposed work can be extend to compare the proposed approachthrough another nature inspired algorithms like particle swam optimization, cuckoosearch algorithm, ant colony algorithm, etc. Moreover, it can be extend to considerfor the tardiness performance measure with the proposed modified GA approach.

Optimization of Energy-Aware Flexible Job Shop Scheduling … 11

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Optimizing the Conveyor Belt Speedof a Bright Annealing Furnace

Akshay Naidu, R. Padmanaban, and R. Vaira Vignesh

Abstract A company’s growth is determined by an increase in its sales numbersand by a decrease in its expenditures. By doing this, the company attains a moresignificant profit (bottom line). In almost all manufacturing industries, annealingfurnaces play a significant role. In this paper, an improvement in the operation ofan annealing furnace used in a press shop is attempted. The press shop performsforging of watch cases, and annealing is performed in between every forging stage.This project focuses on increasing the productivity of the furnace by optimizing itsconveyor belt speed. Experiments are conducted, and several annealed samples werecollected for analysis. Furthermore, hardness and metallographic properties werealso studied. With the help of regression plots of hardness and belt speed, and withthe help of microstructures, an optimized belt speed was selected. The optimizedbelt speed is almost 42% more than the original speed used for production, henceincreasing productivity.

Keywords Annealing · Conveyor belt · Furnace · Hardness · Regression

1 Introduction

The watch manufacturing process involves different stages like designing, proto-typing, tool manufacturing, forging, annealing, machining, polishing, assembly andquality control. During this process, the majority of the problems like impropersurface finish, inadequate reduction in hardness and sensitization while manufac-turing the case centers (body of the watch) and back covers are observed duringannealing. This heat treatment process is seen as a bottleneck in many industries.The annealing softens the watch-case centers and back covers in intermediate stagesto facilitate forging processes. Some of the problems that are encountered are asso-ciated with inefficient usage of the furnace, high energy consumption and improper

A. Naidu · R. Padmanaban (B) · R. Vaira VigneshDepartment of Mechanical Engineering, Amrita School of Engineering, Amrita VishwaVidyapeetham, Coimbatore, Indiae-mail: dr_padmanaban@cb.amrita.edu

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14 A. Naidu et al.

material handling ways leading to safety concerns. In this work, a particular type ofannealing furnace known as a bright annealing furnace is utilized to anneal stainlesssteel components. The old belt speed was set based on the previous experiments,and it was known that it could be improved further. If the belt speed is increased,it will enhance both the top line (sales) and bottom line (profit) of the company byincreasing productivity.

Research works have been carried out to explore the effect of heat treatmentparameters on different materials. Raji et al. [1] report an increase in soaking time ofthe steel, a continual decrease in yield strength, tensile strength, hardness and impactstrength. Thanakijkasem et al. [2] studied the effects of bright annealing on the forma-bility of the SS304 in tube hydroforming (THF). The correct annealing parametersremarkably decreased the development of deformation-induced martensite. Sarkaret al. [3] found the optimum annealing cycle that would produce excellent mechan-ical as well as formability properties of extra deep drawing quality steel. Schinoet al. [4] studied the effect of austenite–martensite transformation and developmentof microstructure after reversion of austenite at various annealing temperatures andtimes for an AISI 304 stainless steel. Annealing at low temperatures resulted inultrafine-grain microstructure, and a Hall–Petch dependency was prevalent.

Singh et al. [5] investigated the effects of a variety of cold rolling on the sensi-tization and intergranular corrosion (IGC) of SS304. The IGC of solution-annealedsamples increased with an increase in sensitization temperature and time. The levelof cold rolling was directly related to the increase in IGC resistance. De et al. [6]proposed amethodology that demonstrates that themartensite transformationmay beeffectively characterized in terms of volume fraction of phases formed during defor-mation through the analysis of a single XRD profile. Milad et al. [7] investigated theeffect of plastic deformation introduced by cold rolling at ambient temperature onthe tensile properties of AISI 304 stainless steel. The results after a 50% reduction inthickness indicate that the formation of strain-induced martensite led to a significantstrengthening. Increase in cold rolling percentage up to 45% increased the tensilestrength, yield strength and hardness. Statistical models based on the design of exper-iments provide a deep insight into the effect of process parameters on the behaviorand properties of the materials processed.

Some of the techniques successfully employed include regression using the designof experiments, Taguchi technique and response surface methodology [8]. When therelation between input parameters and response variable is nonlinear, soft computing-basedmodels like artificial neural network, fuzzy logic and radial basis function havebeen utilized to explore the effect of processes on the properties of materials [9, 10].

The present work aims to optimize the conveyor belt speed of the bright annealingfurnace taking into consideration the specified hardness and microstructure for casecenter of watch. In this study, the influence of belt speed on the microstructuralevolution and microhardness of the specimen was investigated. An optimum beltspeed was established based on the results of the developed model.

Optimizing the Conveyor Belt Speed of a Bright Annealing Furnace 15

2 Materials and Methods

2.1 Material

SS304 is used for manufacturing case centers. Holes are made in the long strip ofSS304 and are blanked into required dimensions. These blanked components areforged at several stages in the press shop to obtain the final shape. The workflow inthe press shop is as follows:

1. Blanking2. Pre-cleaning and cleaning3. Annealing4. Oiling5. Forming6. Repeat from step 2 for consecutive forming process.

In this study, the case center is obtained after eight stages of forging process. Asample image of the case center that is formed after eight forging stages is shown inFig. 1.

AnnealingAnnealing is carried out after each forging stage to remove the internal and residualstresses. KOHNLE/BENCO bright annealing furnace was used for the annealingprocess. It is a conveyor belt-type continuous furnace with three heating zones andtwo cooling chambers. It has two sets of SS continuous belts that can be operatedat different speeds. The current operating belt speed is 60 cm/min, and the peaktemperature is 1120 °C. The inlet temperature of the cooling water in the cooling

Fig. 1 Front and back view of model 3072 CC