Design of Experiments in Production Engineering

201 Pages·2016·4.77 MB·English

by  J. Paulo Davim (eds.)

Management and Industrial Engineering
J. Paulo Davim Editor
Design of
Experiments
in Production
Engineering
Management and Industrial Engineering
Series editor
J. Paulo Davim, Aveiro, Portugal
More information about this series at http://www.springer.com/series/11690
J. Paulo Davim
Editor
Design of Experiments
in Production Engineering
123
Editor
J.PauloDavim
Department ofMechanical Engineering
University of Aveiro
Aveiro
Portugal
ISSN 2365-0532 ISSN 2365-0540 (electronic)
ManagementandIndustrial Engineering
ISBN978-3-319-23837-1 ISBN978-3-319-23838-8 (eBook)
DOI 10.1007/978-3-319-23838-8
LibraryofCongressControlNumber:2015948765
SpringerChamHeidelbergNewYorkDordrechtLondon
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Preface
Nowadays, it is the trend to report production engineering as a combination of
manufacturing technology with applied management science. This book covers
designofexperiments(DoE)appliedinproductionengineering.DoEisastatistical
methodologyusedtoestablishstatisticalcorrelationbetweenasetofinputvariables
with a chosen outcome of the system/process. DoE is a systematic approach to
investigationofasystem/process.Ingeneral,DoEanalysingandinterpretingsetsof
experiments without incurring a too high cost or taking too much time.
Thepurposeofthisbookistopresentacollectionofexamplesillustrating DoE
appliedinproductionengineering.Thefirstchapteris“Screening(Sieve)Designof
ExperimentsinMetalCutting”.Thesecondchapteris“ModellingandOptimisation
of Machining with the Use of Statistical Methods and Soft Computing”. The third
chapter is “Design of Experiments—Statistical and Artificial Intelligence Analysis
fortheImprovementofMachiningProcesses:AReview”.Thefourthchapteris“A
Systematic Approach to Design of Experiments in Waterjet Machining of High
PerformanceCeramics”.Thefifthchapteris“ResponseSurfaceModelingofFractal
DimensioninWEDM”.Thesixthchapteris“ThrustForceandTorqueMathematical
Models in Drilling of Al7075 Using the Response Surface Methodology”. The
seventh chapter is “Design of Experiments in Titanium Metal Cutting Research”.
Finally,theeighthchapteris“ParametricOptimizationofSubmergedArcWelding
Using Taguchi Method”.
This book can be usedas a research book for a final undergraduate engineering
course or as a topic on DoE in production engineering at the postgraduate level.
Also, this book can serve as a valuable reference for academics, engineers,
researchers, professionals in production engineering and related subjects. The sci-
entific interest in this book is obvious for many important centres of research and
universitiesaswellasindustry.Therefore,itisexpectedthatthisbookwillmotivate
others to undertake research in DoE in production engineering.
v
vi Preface
TheEditoracknowledgesSpringerforthisopportunityandfortheirenthusiastic
and professional support. Finally, I would like to thank all the chapter authors for
their availability for this work.
Aveiro, Portugal J. Paulo Davim
October 2015
Contents
Screening (Sieve) Design of Experiments in Metal Cutting. . . . . . . . . . 1
Viktor P. Astakhov
Modelling and Optimization of Machining with the Use
of Statistical Methods and Soft Computing. . . . . . . . . . . . . . . . . . . . . 39
Angelos P. Markopoulos, Witold Habrat, Nikolaos I. Galanis
and Nikolaos E. Karkalos
Design of Experiments—Statistical and Artificial Intelligence
Analysis for the Improvement of Machining Processes: A Review . . . . 89
Carlos H. Lauro, Robson B.D. Pereira, Lincoln C. Brandão
and J.P. Davim
A Systematic Approach to Design of Experiments in Waterjet
Machining of High Performance Ceramics . . . . . . . . . . . . . . . . . . . . . 109
Flaviana Tagliaferri, Markus Dittrich and Biagio Palumbo
Response Surface Modeling of Fractal Dimension in WEDM. . . . . . . . 135
Prasanta Sahoo and Tapan Kr. Barman
Thrust Force and Torque Mathematical Models in Drilling
of Al7075 Using the Response Surface Methodology . . . . . . . . . . . . . . 151
Panagiotis Kyratsis, Cesar Garcia-Hernandez, Dimitrios Vakondios
and Aristomenis Antoniadis
Design of Experiments in Titanium Metal Cutting Research . . . . . . . . 165
Navneet Khanna
Parametric Optimization of Submerged Arc Welding
Using Taguchi Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
S. Vinodh, S. Karthik Bharathi and N. Gopi
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
vii
Nomenclature
AI Artificial Intelligence
AISI American Iron and Steel Institute
ANFIS Adaptive Neuro-Fuzzy Inference System
ANN Artificial Neural Network
ANOVA Analysis of Variance
BN Bayesian Networks
CAM Computer Aided Manufacturing
CNC Computer Numerical Control
DoE Design of Experiments
EDM Electrical Discharge Machining
FFD Full Factorial Design
FL Fuzzy Logic
GA Genetic Algorithm
MRPD Multivariate Robust Parameter Design approach
OA Orthogonal Array
rpm Revolutions per minute
RSM Response Surface Methodology
SSA Singular Spectrum Analysis
ix
Screening (Sieve) Design of Experiments
in Metal Cutting
Viktor P. Astakhov
This chapter discuses particularities of the use of DOE in experimental studies of
metal cutting. It argues that although the cost of testing in metal cutting is high,
there is no drive to improve or generalize the experimental results. It explains that
full factorial design of experiments and the most advanced group method of data
handling (known as GMDH) method allow accurate estimation of all factors
involved and their interactions. The cost and time needed for such tests increase
with the number of factors considered. To reduce these cost and time, two-stage
DOE procedure to be used in metal cutting experimental studies is suggested:
screening DOE in the first stage and full factorial DOE in the second stage. The
Plackett and Burman DOE is found to be very useful in screening tests in metal
cutting studies.
1 Introduction
Although machining is one of the oldest manufacturing processes, most essential
characteristics and outcomes of this process such as tool life, cutting forces,
integrityofthemachinedsurface,andenergyconsumptioncanonlybedetermined
experimentally. As a result, new improvements in the tool, machine and process
design/optimization, and implementation of improved cutting tool materials are
justified through a series of experimental studies. Unfortunately, experimental
studiesinmetalcuttingareverycostlyandtime-consumingrequiringsophisticated
equipment and experienced personnel. Therefore, the proper test strategy, meth-
odology, data acquisition, statistical model construction, and verification are of
prime concern in such studies.
Metal cutting tests have been carried out in systematic fashion over at least
150 years, in tremendously increasing volume. However, most of the tests carried
out so far have been conducted using a vast variety of cutting conditions and test
V.P.Astakhov(&)
GeneralMotorsBusinessUnitofPSMi,1255BeachCt.,Saline,MI48176,USA
e-mail:[email protected]
©SpringerInternationalPublishingSwitzerland2016 1
J.P.Davim(ed.),DesignofExperimentsinProductionEngineering,
ManagementandIndustrialEngineering,DOI10.1007/978-3-319-23838-8_1