Optimization of Manufacturing Processes

237 Pages·2020·7.479 MB·English

by  Kapil Gupta, Munish Kumar Gupta

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
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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