Table Of ContentCOMPUTATIONAL
INTELLIGENCE IN
MANUFACTURING
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Woodhead Publishing Reviews:
Mechanical Engineering Series
COMPUTATIONAL
INTELLIGENCE IN
MANUFACTURING
Edited by
KAUSHIK KUMAR
GANESH KAKANDIKAR
J. PAULO DAVIM
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Contents
Contributors ix
Preface xiii
1. Multiversemultiobjective optimization of thinningand
wrinkling in automotiveconnector 1
GaneshM. Kakandikar, Vilas M. Nandedkar,and OmkarK. Kulkarni
1.1 Sheet-metalforming 1
1.2 Automotivecomponentunderstudy:Connector 1
1.3 Taguchidesignofexperiments 2
1.4 Numericalsimulationresults—Connector 3
1.5 Connector—Formingzone 6
1.6 Connector—Thicknessdistribution 6
1.7 Connector—Safetyzone 9
1.8 Connector—Safetymargin 9
1.9 Connector—Forminglimitdiagram 11
1.10 Analysisofvarianceforthinning—Connector 11
1.11 Analysisofvarianceforwrinkling—Connector 15
1.12 Linearregressionanalysis 16
1.13 Mathematicalmodeling—Connector 16
1.14 Problemformulation—Thinningandwrinkling 17
1.15 MultiobjectiveMultiverseoptimizationalgorithm(MOMVO) 17
1.16 Multiobjectiveoptimization 20
1.17 Conclusion 21
References 21
2. Anapproach for machining curve cooling hole in plastic
injection mold 23
DiweshBabruwan Meshram
2.1 Introduction 23
2.2 Methodology 28
2.3 Experimentation 33
2.4 Resultsanddiscussion 37
2.5 Conclusions 42
References 43
v
vi Contents
3. Experimental and numerical investigation of deformation
behaviorofdualphasesteelatelevatedtemperaturesusing
various constitutive models and ANN 47
SandeepPandre,Ayush Morchhale, NitinKotkunde,
SwadeshKumarSingh, NavneetKhanna,and Ambuj Saxena
3.1 Introduction 47
3.2 Experimentalprocedure 50
3.3 Resultsanddiscussion 53
3.4 Conclusions 67
References 68
4. Optimization ofthermalefficiency of Scheffler solar
concentrator receiverusing slime mold algorithm 71
AnitaNeneand OmkarK. Kulkarni
4.1 Introduction 71
4.2 Experimentalsetup 72
4.3 Experimentationforthermalefficiency 72
4.4 Slimemoldoptimizationalgorithm 76
4.5 Resultsanddiscussions 83
4.6 Conclusion 84
References 85
5. Study ondrilling behavior of polymer nanocomposites
modified bycarbon nanomaterial with fiber: Acase
study 87
JogendraKumar, KesarwaniShivi, Balram Jaiswal, Kaushlendra Kumar,
DevendraKumar Singh,Kuldeep Kumar,Rahul Vishwakarma, and
RajeshKumar Verma
5.1 Introduction 87
5.2 Machiningprocess(drilling)onlaminatedpolymer
nanocomposite 90
5.3 MachiningprocesscontrolthroughMCDM/algorithm
approach 93
5.4 Drillingoflaminatedpolymernanocomposite:
Acasestudy 94
5.5 Summaryofchapter 103
Acknowledgments 104
References 104
Contents vii
6. Machiningperformanceanalysisofmicro-EDmillingprocessof
titanium alloy (Ti-6Al-4V) 111
P.Sivaprakasam, P.Hariharan,S.Gowri, andJ. Udaya Prakash
6.1 Introduction 111
6.2 Experimentaldetails-materialsandmethods 114
6.3 Resultsanddiscussion 115
6.4 Conclusions 127
References 127
7. Computational analysis of provisionalstudy on white layer
properties by EDM vs. WEDM ofaluminum metal matrix
composites 131
M.Kathiresan, R.Theerkka Tharisanan,and P.Pandiarajan
7.1 Introduction 131
7.2 Heat-affectedzone(HAZ)onEDM 133
7.3 Heat-affectedzone(HAZ)onWEDM 134
7.4 WhitelayeronEDMandWEDM 135
7.5 ThermophysicalmodelforEDM 136
7.6 ThermophysicalmodelforWEDM 137
7.7 Methodologyofcompositemaking 139
7.8 Machiningsetup 142
7.9 Resultsanddiscussion 144
7.10 Conclusion 155
References 156
8. Scopeofindustry4.0componentsinmanufacturingSMEs 161
AroraMonika, Buttan Apoorva, KumarAnuj, PujariPurvi, and
SabharwalJyotsana
8.1 Introduction 161
8.2 WhatisAI? 161
8.3 DifferencebetweenCIandAI 162
8.4 PrinciplesofCI 162
8.5 Neuralnetwork 163
8.6 Deeplearning 163
8.7 Probabilisticmethodforuncertainreasoning 163
8.8 Smallandmediumenterprises(SMEs) 164
8.9 Industry4.0 164
8.10 SMEsandindustry4.0 165
viii Contents
8.11 Artificialintelligence 165
8.12 BarrierstoadoptionofcomputationalintelligenceinSMEs 166
8.13 ComputationalintelligenceandSMEs 166
8.14 Modelformulation(datalayer/informationlayer/intelligence
layer) 169
8.15 Modeldiscussion 169
8.16 ApplicationsofCIinSMEs 170
8.17 Conclusion 171
References 173
9. Processparameteroptimizationinmanufacturingofrootcanal
device using gorilla troopsoptimization algorithm 175
SwanandPachpore, PradeepJadhav, andRatnakar Ghorpade
9.1 Introduction 175
9.2 Mathematicalmodel 177
9.3 Gorillatroopsoptimizationforslidingfrictionalforce 179
9.4 Explorationphase 180
9.5 Exploitationphase 181
9.6 Resultsanddiscussion 182
9.7 Conclusion 184
References 184
10. A comprehensivereview ofagriculture irrigation using
artificial intelligence for crop production 187
Maithili AnilChougule and AnilS.Mashalkar
10.1 Introduction 187
10.2 Influenceofartificialintelligencetechnologyonanagriculture
irrigationsystem 189
10.3 Embeddedroboticsandautonomousinagriculture 189
10.4 Smartirrigationsystemsinagriculture 190
10.5 Weedingmanagementsystems 193
10.6 Conclusion 196
10.7 Futurescope 197
References 197
Index 201
Contributors
MaithiliAnilChougule
SchoolofMechanicalEngineering,DrVishwanathKaradMITWorldPeaceUniversity,
CAD/CAM/CAE,Pune,India
KumarAnuj
ApeejaySchoolofManagement,Delhi,India
ButtanApoorva
AmitySchoolofCommunication,AmityUniversity,Noida,India
RatnakarGhorpade
DepartmentofMechanicalEngineering,CollegeofEngineering,BharatiVidyapeeth
(Deemedtobe)University,Pune,India
S.Gowri
DepartmentofManufacturingEngineering,CollegeofEngineeringGuindy,Anna
University,Chennai,India
P.Hariharan
DepartmentofManufacturingEngineering,CollegeofEngineeringGuindy,Anna
University,Chennai,India
PradeepJadhav
SchoolofMechanicalEngineering,Dr.VishwanathKaradMITWorldPeaceUniversity,
Pune,India
BalramJaiswal
MaterialsandMorphologyLaboratory,DepartmentofMechanicalEngineering,Madan
MohanMalaviyaUniversityofTechnology,Gorakhpur,UttarPradesh,India
SabharwalJyotsana
ApeejaySchoolofManagement,Delhi,India
GaneshM.Kakandikar
SchoolofMechanicalEngineering,Dr.VishwanathKaradMITWorldPeaceUniversity,
Pune,India
M.Kathiresan
DepartmentofMechanicalEngineering,E.G.S.PillayEngineeringCollege,Nagapattinam,
India
NavneetKhanna
AdvancedManufacturingLaboratory,InstituteofInfrastructureTechnologyResearchand
Management(IITRAM),Ahmedabad,India
NitinKotkunde
DepartmentofMechanicalEngineering,BITSPilani,Hyderabad,India
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