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Optimization of Manufacturing Processes PDF

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Springer Series in Advanced Manufacturing Kapil Gupta Munish Kumar Gupta Editors Optimization of Manufacturing Processes Springer Series in Advanced Manufacturing Series Editor Duc Truong Pham, University of Birmingham, Birmingham, UK The Springer Series in Advanced Manufacturing includes advanced textbooks, researchmonographs,editedworksandconferenceproceedingscoveringallmajor subjects in the field of advanced manufacturing. The following is a non-exclusive list of subjects relevant to the series: 1. Manufacturing processes and operations (material processing; assembly; test and inspection; packaging and shipping). 2. Manufacturing product and process design (product design; product data management; product development; manufacturing system planning). 3. Enterprise management (product life cycle management; production planning and control; quality management). Emphasiswillbeplacedonnovelmaterialoftopicalinterest(forexample,books on nanomanufacturing) as well as new treatments of more traditional areas. As advanced manufacturing usually involves extensive use of information and communication technology (ICT), books dealing with advanced ICT tools for advanced manufacturing are also of interest to the Series. Springer and Professor Pham welcome book ideas from authors. Potential authors who wish to submit a book proposal should contact Anthony Doyle, Executive Editor, Springer, e-mail: [email protected]. More information about this series at http://www.springer.com/series/7113 Kapil Gupta Munish Kumar Gupta (cid:129) Editors Optimization of Manufacturing Processes 123 Editors KapilGupta Munish KumarGupta Department ofMechanical andIndustrial Assistant Professor, University Center Engineering Technology for Research andDevelopment University of Johannesburg Chandigarh University,Gharuan Doornfontein, Johannesburg, SouthAfrica Mohali,India ISSN 1860-5168 ISSN 2196-1735 (electronic) SpringerSeries inAdvancedManufacturing ISBN978-3-030-19637-0 ISBN978-3-030-19638-7 (eBook) https://doi.org/10.1007/978-3-030-19638-7 ©SpringerNatureSwitzerlandAG2020 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained hereinorforanyerrorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregard tojurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface InthiseraoftheFourthIndustrialRevolution,intelligentmanufacturingisaglobal trend to simultaneously optimize quality, productivity, and sustainability. Optimizationofmanufacturingprocessesandsystemsispossiblewiththeavailable Industry 4.0 tools and techniques. This book provides a detailed understanding on optimization of various manufacturing processes and systems using some of the important statistical and evolutionary (soft computing or can be called as Industry 4.0 based) techniques. It covers sufficient theoretical details, salient features, implementation steps, effectiveness and outcomes of statistical, multi-criteria decision-making and evolutionary techniques for single and multi-objective optimization to improve quality, productivity, and sustainability in manufacturing. Thisbookconsistsofninechaptersonoptimizationofmanufacturingprocesses. Chapter “Modelling and Optimization of Alpha-set Sand Moulding System Using Statistical Design of Experiments and Evolutionary Algorithms” sheds light on modelling and optimization of sand moulding system by GA-, PSO-, and TLBO-type evolutionary techniques. Chapter “Optimization of Electric Discharge Machining Based Processes” provides comprehensive information on optimization of electric discharge machining-based processes via theory, the literature review, andcasestudies.Chapter “OptimizationofAccuracyandSurfaceFinishofDrilled Holes in 350 Mild Steel” details a statistical optimization of hole quality characteristics in drilling of mild steel. A case study on response surface methodology-based modelling and desirability optimization of laser additive manufacturingoftitaniumisdiscussedinChapter “ModellingandOptimizationof Laser Additive Manufacturing Process of Ti Alloy Composite”. Chapter “PredictionandOptimizationofTensileStrengthinFDMbased3DPrintingUsing ANFIS” discusses the optimization of mechanical properties of 3D printed parts using ANFIS. Chapter “Optimization of Abrasive Water Jet Machining for Green Composites using Multi-variant Hybrid Techniques” focuses on multi-objective optimization of abrasive water jet machining with the help of MOORA, GA, TOPSIS, and DEAR methods. Machining condition optimization while turning titanium using integrated fuzzy MOORA method is given in Chapter “An Integrated Fuzzy-MOORA Method for the Selection of Optimal Parametric v vi Preface Combination in Turing of Commercially Pure Titanium”. Chapter “Application of Multi-objective Genetic Algorithm (MOGA) Optimization in Machining Processes” provides a review of the literature on implementation of genetic algo- rithm (GA) for quality optimization of different machining processes. Chapter “Optimization inManufacturingSystems Using Evolutionary Techniques”focuses on optimization of manufacturing systems by GA and particle swarm optimization (PSO) techniques. We sincerely acknowledge Springer for this opportunity and their professional support. Finally, we would like to thank all the chapter contributors for their availability and valuable contributions. Johannesburg, South Africa Kapil Gupta March 2019 Munish Kumar Gupta Contents Modelling and Optimization of Alpha-set Sand Moulding System Using Statistical Design of Experiments and Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 G. C. Manjunath Patel, Ganesh R. Chate and Mahesh B. Parappagoudar Optimization of Electric Discharge Machining Based Processes. . . . . . . 29 Roan Kirwin, Aakash Niraula, Chong Liu, Landon Kovach and Muhammad Jahan Optimization of Accuracy and Surface Finish of Drilled Holes in 350 Mild Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 A. Pramanik, A. K. Basak, M. N. Islam, Y. Dong, Sujan Debnath and Jay J. Vora ModellingandOptimizationofLaserAdditiveManufacturingProcess of Ti Alloy Composite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Rasheedat M. Mahamood and Esther T. Akinlabi Prediction and Optimization of Tensile Strength in FDM Based 3D Printing Using ANFIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Shilpesh R. Rajpurohit and Harshit K. Dave OptimizationofAbrasiveWaterJetMachiningforGreenComposites Using Multi-variant Hybrid Techniques. . . . . . . . . . . . . . . . . . . . . . . . . 129 G. C. Manjunath Patel, Jagadish, Rajana Suresh Kumar and N. V. Swamy Naidu An Integrated Fuzzy-MOORA Method for the Selection of Optimal Parametric Combination in Turing of Commercially Pure Titanium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Akhtar Khan, Kalipada Maity and Durwesh Jhodkar vii viii Contents Application of Multi-objective Genetic Algorithm (MOGA) Optimization in Machining Processes. . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Nor Atiqah Zolpakar, Swati Singh Lodhi, Sunil Pathak and Mohita Anand Sharma Optimization in Manufacturing Systems Using Evolutionary Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Ravi Shankar Rai and Vivek Bajpai Index .... .... .... .... .... ..... .... .... .... .... .... ..... .... 231 Modelling and Optimization of Alpha-set Sand Moulding System Using Statistical Design of Experiments and Evolutionary Algorithms G.C.ManjunathPatel,GaneshR.ChateandMaheshB.Parappagoudar Abstract Thetraditionaltrial-anderrormethodappliedtoderiveempiricalrelation andoptimizetheprocessistimeconsumingandresultsinreducedproductivity,high rejectionandcost.Hence,currentresearchinfoundriesfocussedtowardsdevelop- ment of statistical modelling and optimization tools. The present research work is focused on modelling and optimization of Alpha-set moulding sand system. The variables such as percent of resin and hardener, and curing time will influence the sandmouldproperties,namely,compressionstrength,permeability,mouldhardness, gasevolutionandcollapsibility.ExperimentaldataiscollectedasperCCDdesign matrixandnon-linearmodelshavebeendevelopedforallresponses.Thebehaviour ofallresponsesisstudiedbyutilizingsurfaceplots.Thestatisticaladequacyofall modelsistestedwithhelpofANOVA.Allresponsesaretestedfortheirprediction capacitywiththehelpoftestcases.Thepredictivenon-linearmodels,developedfor the process resulted in average deviation of less than 5%. The optimization (GA, PSO, DFA and TLBO) tools are applied to optimize the process for conflicting requirementsinsandmouldproperties.Sixcasestudieswithdifferentcombination ofweightfractionsassignedtosandmouldpropertiesareconsidered.Theoptimum solutioncorrespondtohighestcompositedesirabilityvalueisselected.TLBOout- performedotheroptimizationtools(i.e.GA,PSO,andDFA)whiledeterminingthe highest desirability value and resulted in optimized sand mould properties. Exper- iments are conducted for the optimized and normal (i.e. lowest desirability) sand mouldconditions.CastingsarepreparedbypouringmoltenLM20alloytothepre- paredmoulds.Thecastingobtainedfortheoptimizedsandmouldconditionresulted inabettercastingquality. B G.C.ManjunathPatel( ) DepartmentofMechanicalEngineering,PESInstituteofTechnologyandManagement, Shivamogga577204,Karnataka,India e-mail:[email protected] G.R.Chate DepartmentofIndustrialandProductionEngineering,K.L.S.GogteInstituteofTechnology, Belgaum,India M.B.Parappagoudar DepartmentofMechanicalEngineering,PadreConceicaoCollegeofEngineering, Verna,Goa,India ©SpringerNatureSwitzerlandAG2020 1 K.GuptaandM.K.Gupta(eds.),OptimizationofManufacturingProcesses,Springer SeriesinAdvancedManufacturing,https://doi.org/10.1007/978-3-030-19638-7_1

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