Axiomatic quality : integrating axiomatic design with six-sigma, reliability, and quality engineering

293 Pages·2005·2.06 MB·English

by  Basem Said El-Haik

AXIOMATIC QUALITY
Integrating Axiomatic Design with
Six-Sigma, Reliability, and
Quality Engineering
BASEM SAID EL-HAIK
With Forewordby Nam P. Suh
AJOHNWILEY&SONS,INC.,PUBLICATION
Copyright2005byJohnWiley&Sons,Inc.Allrightsreserved.
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LibraryofCongressCataloging-in-PublicationData:
El-Haik,Basem.
Axiomaticquality:integratingaxiomaticdesignwithsix-sigma,reliability,
andqualityengineering/byBasemSaidEl-Haik.
p.cm.
“Wiley-Intersciencepublication.”
Includesbibliographicalreferencesandindex.
ISBN0-471-68273-X(cloth:alk.paper)
1.Processcontrol.2.Qualitycontrol.3.Sixsigma(Qualitycontrolstandard).
4.Productionmanagement.I.Title.
TS156.8.E442005
658.5(cid:1)62–dc22
2004059408
PrintedintheUnitedStatesofAmerica.
10987654321
CONTENTS
1 INTRODUCTIONTOTHEAXIOMATICQUALITYPROCESS 1
1.1 Why Axiomatic Quality? 1
1.2 Goals and Scope of the Book 3
1.3 Axiomatic Design 3
1.4 Six-Sigma and Design for Six-Sigma Philosophy 9
1.4.1 Introduction to Design for Six-Sigma 11
1.5 Robustness Engineering: Taguchi’s Quality Engineering 12
1.6 Problems Addressed by Axiomatic Quality 15
1.7 Introduction to the Axiomatic Quality Process 16
1.8 Axiomatic Quality in Product Development 17
1.9 Summary 19
2 AXIOMATICDESIGNMETHOD 21
2.1 Introduction 21
2.2 Axiomatic Design Method 24
2.2.1 Design Domains 25
2.2.2 Design Hierarchy and Zigzagging Process 27

Acclaro DFSS Light
ftp://ftp.wiley.com/public/sci tech med/axiomatic quality/ 27
2.3 Introduction to the Independence Axiom 29
2.4 Introduction to the Information Axiom 30
2.5 Axiomatic Design Theorems and Corollaries 32
2.5.1 Axiomatic Design Corollaries 33
2.5.2 Axiomatic Design Theorems of General Design 34
2.5.3 Theorems for Design of Large Systems 36
2.6 Case Study: Depth Charge Initiator (Nordlund, 1996) 36
2.7 Summary 43
3 INDEPENDENCEAXIOM 44
3.1 Introduction 44
3.2 Independence Axiom and the Zigzagging Approach 46
3.2.1 Coupling Measures 48
3.3 Design Mappings and Design Structures 49
3.4 Case Study 1: Axiomatic Design of a Water Faucet
(Swenson and Nordlund, 1995) 50
3.5 Case Study 2: Implementation Methodology for Transition
from Traditional to Cellular Manufacturing Using Axiomatic
Design (Durmusoglu et al., 2002) 55
3.5.1 Axiomatically Driven Cellular Manufacturing System 56
3.6 Summary 60
4 INFORMATIONAXIOMANDDESIGNCOMPLEXITY 62
4.1 Introduction 62
4.2 Traditional Formulation of the Information Axiom:
Suh’s Definition 64
4.2.1 Complexity Reduction Techniques 65
4.3 Complexity Vulnerability 71
4.4 Theoretical Foundation of the New Complexity Theory 72
4.5 New Complexity Theory 74
4.5.1 Coupled Design Complexity 76
4.6 Complexity Due to Statistical Correlation 77
4.7 Summary 83
5 QUALITYENGINEERING:AXIOMATICPERSPECTIVE 85
5.1 Introduction 85
5.2 Robust Design (Quality Engineering): Overview 86
5.3 Mathematical Relationship between the Quality Loss Function
and Axiomatic Measures 88
5.4 Mathematical Relationship between the Quality Loss Function
and Axiomatic Measures of Higher Modularity 92
5.4.1 Equal Variance 95
5.4.2 Equal Sensitivity 96
5.5 Estimation of the Expected Loss Function 102
5.6 Mathematical Relationship between the Signal-to-Noise Ratio
and Axiomatic Measures 103
5.7 Summary 104
6 AXIOMATICQUALITYANDRELIABILITYPROCESS 106
6.1 Introduction 106
6.2 Axiomatic Quality Process 108
6.2.1 Why the Axiomatic Quality Process? 109
6.2.2 Axiomatic Quality Process Map 112
6.2.3 Axiomatic Quality Design Team 114
6.3 Customer Attributes-to-FRs Mapping: Understanding the
Voice of the Customer 115
6.3.1 QFD Stage 1 121
6.3.2 QFD Stage 2 123
6.4 Conceptual Design for Capability Phase 125
6.4.1 Define FR Specification Target Values and Allowable
Tolerances 125
6.5 Option A: Conceptual Design for the Capability Phase of an
Incremental Design 126
6.5.1 Step A.1: Perform the Physical Mapping (Design
Analysis) 127
6.5.2 Step A.2: Perform the Physical Mapping (Design
Synthesis) 128
6.5.3 Step A.3: Uncouple or Decouple the Design
Mappings 131
6.5.4 Step A.4: Conduct Axiomatic Quality Concept
Selection 131
6.5.5 Step A.5: Detail the Structures 132
6.5.6 Step A.6: Prepare for the Optimization Phase of the
Structure Selected 133
6.6 Option B: Conceptual Design for the Capability Phase of a
Creative Design 135
6.6.1 Step B.1: Define the Pursuit Ideal Product 135
6.6.2 Step B.2: Understand and Project Product
Evolution 136
6.6.3 Step B.3: Initial Concept Generation 137
6.7 Axiomatic Quality Optimization Phase 138
6.8 Axiomatic Quality Process Deployment 139
7 AXIOMATICQUALITYPROCESSCONCEPTSELECTION
PROCESS 145
7.1 Introduction 145
7.2 Design Feasibility in Axiomatic Quality 147
7.2.1 Modules 147
7.2.2 Design Technical (Morphological) Feasibility 148
7.3 Concept Selection Problem 151
7.4 Concept Selection Fuzzy Modeling 155
7.4.1 Fuzzy Concepts 155
7.4.2 Possibility–Probability Consistency Principle 156
7.4.3 Maximum Entropy Formulation 157
7.5 Axiomatic Quality Fuzzy Concept Selection
Formulation 160
7.5.1 Case Study: Global Commercial Process 161
7.6 Summary 170
8 CONCEPTUALDESIGNFORCAPABILITYPHASE 171
8.1 Introduction 171
8.2 Problems That Can Be Solved by Axiomatic Quality 172
8.3 Conceptual Design for the Capability Phase 173
8.3.1 Implication of Coupling in the CDFC Phase 174
8.3.2 Step A.3: Uncouple or Decouple the Design
Mappings 177
8.4 Case Study: Transmission Vane Oil Pump CDFC 181
8.4.1 Pump Zigzagging Process 183
8.4.2 Decoupling Phase 189
8.4.3 Step A.5: Detail the Design 190
8.5 Theory of Inventive Problem Solving 192
8.5.1 TRIZ in the Axiomatic Quality Process 194
8.6 Summary 197
Appendix 8A: Design Matrixes 197
9 AXIOMATICQUALITYOPTIMIZATIONPHASE 201
9.1 Introduction 201
9.2 Axiomatic Quality Operational Vulnerability Optimization 203
9.2.1 Vulnerability Optimization Routine 205
9.3 Parameter Design Optimization 209
9.3.1 Noise Factors Identification 210
9.3.2 Parameter Design Optimization DOEs 211
9.3.3 Data Collection and Results Analysis 212
9.3.4 Case Study: Axiomatic Quality Parameter Design 213
9.4 Axiomatic Quality Strategy in the Tolerance Optimization
Phase 218
9.4.1 Robustness at Six-Sigma Quality: Signal-to-Noise
Ratio and Quality Loss Function 219
9.5 Design Operational Vulnerability Optimization Using
Tolerances of Uni-FR Design Modules 224
9.5.1 Coupling Vulnerability Constraint 226
9.5.2 Meaning of the Solution 227
9.6 Design Operational Vulnerability Optimization Using
Tolerances of an FR Array 228
9.7 Summary 231
Appendix 9A: Proof of Theorem 9.1 231
10 CASESTUDY:LOW-PASSFILTERAXIOMATICQUALITY
PROCESS 233
10.1 Introduction 233
10.2 Problem Statement 234
10.3 Passive Filter Conceptual Design for the Capability
Phase 235
10.4 Passive Filter Tolerance Optimization Phase 238
11 AXIOMATICRELIABILITY 243
11.1 Introduction 243
11.2 Why Axiomatic Reliability? 245
11.3 Axiomatic Reliability in the Development Cycle 246
11.4 Axiomatic Reliability in the Design Stages 247
11.4.1 Linear, Independent, Uncoupled Design 256
11.4.2 Linear, Independent, Decoupled Design 258
11.5 Case Study: Passive Filter Design 260
11.6 Physical Structure Axiomatic Reliability Formulation 265
11.6.1 Time-Dependent Physical Structure Axiomatic
Reliability Assessment 267
11.7 Physical Structure Axiomatic Importance Formulation 269
11.7.1 Structured Modules 269
11.8 Design for Reliability 270
11.9 Summary 272
REFERENCES 273
INDEX 281
CHAPTER 1
INTRODUCTION TO THE AXIOMATIC
QUALITY PROCESS
1.1 WHYAXIOMATICQUALITY?
Attention has begun to shift from the improvement of design quality in down-
stream development stages to early upstream stages. This shift is motivated by
the fact that the design decisions made during early stages of a product develop-
ment cycle have the largest impact on total life-cycle cost and the quality of the
system. It has been claimed that as much as 80% of the total life-cycle cost is
determined during the concept development stage (Fredrikson, 1994). Research
in the design and manufacturing arenas, including product development, is cur-
rently receiving anincreasedfocus on addressing industry efforts to shorten lead
times, cut development and manufacturing costs, lower total life-cycle cost, and
improvethequalityoftheendproductsandsystems.Itistheauthor’sexperience
that at least 80% of design quality is also determined in the early design phases.
In the context of this book, the term quality can be defined as the degree
to which the design vulnerabilities do not adversely affect product performance.
This definition, as well as most of the developments of this book, are equally
applicable to the service design arenabecause the design principles, in particular
those promoted to axioms,1 are universal. In the context of the axiomatic quality
1Fundamental knowledge that cannot be tested, yet is generally accepted as true, is treated as an
axiom.
AxiomaticQuality:IntegratingAxiomaticDesignwithSix-Sigma,Reliability,and
QualityEngineering,byBasemSaidEl-Haik
ISBN0-471-68273-X Copyright2005JohnWiley&Sons,Inc.
1
2 INTRODUCTIONTOTHEAXIOMATICQUALITYPROCESS
process, the major design vulnerabilities are be categorized as:
ž Conceptual vulnerabilities, established due to the violation of design prin-
ciples.
ž Operationalvulnerabilities,createdasaresultoffactorsbeyondthedesign-
er’s control, called noise factors. These factors, in general, are responsi-
ble for causing a product’s functional characteristic or process to deviate
from target values. Controlling noise factors is very costly or difficult, if
not impossible. Operational vulnerability is usually addressed by robust
design (Taguchi et al., 1989).
Conceptualvulnerabilitieswillalwaysresultinoperationalvulnerabilities.How-
ever, the reverse is not true. That is, it is possible for a healthy concept that
is in full obedience to design principles to be operationally vulnerable. Concep-
tual vulnerabilities are usually overlooked during product development, due to a
lack of understanding of the principles of design, the absence of a compatible
systemic approach to finding ideal solutions, the pressure of deadlines, and bud-
get constraints. These vulnerabilities are usually addressed by traditional quality
methods.Thesemethodscanbecharacterizedasafter-the-factpracticessincethey
use lagging information relative to developmental activities such as bench tests
and field data. Unfortunately, these practices drive development toward endless
design–test–fix–retest cycles, creating what is broadly known in the manufac-
turing industry as a fire fighting operational mode. Companies that follow these
practicesusuallysufferfromhighdevelopmentcosts,longertimetomarket,lower
quality levels, and marginal competitive edge. In addition, firefighting actions to
improve conceptual vulnerabilities arenot only costly but also difficult to imple-
ment,aspressuretoachievedesignmilestonesbuildsduringthedevelopmentcycle.
Therefore,itshouldbeagoaltoimplementqualitythinkingintheconceptualstages
ofadevelopmentcycle.Thisgoalcanbeachievedwhensystematicdesigntheories
areintegratedwithqualityconceptsandmethodsupfront.Specifically,thisbookis
gearedtowarddevelopinganintegrationframeworkorprocessforqualityindesign
byborrowingfromqualityengineering(Taguchi,1986)andtheaxiomaticdesign
principlesofSuh(1990).Inthecontextofthisbook,thisframeworkisreferredto
asaxiomaticquality.Theobjectiveaxiomaticqualityprocessistoaddressdesign
vulnerabilities,bothconceptualandoperational,byprovidingtools,processes,and
formulationsfortheirquantification,theneliminationorreduction.
Operationalvulnerabilitiesnecessitatethepursuitofvariabilityreductionasan
objective. Variability reduction has been a popular field of study. The method of
robustdesignadvancedby Taguchi(1986),TaguchiandWu(1980),andTaguchi
et al. (1989), time-domain control theory (Dorf, 2000), and tolerance design and
the tolerancing technique2 are just some of the proposals in this area. Tolerance
researchincludesmanyareasthatdealwiththeassignmentoftolerancestodesign
2Somedevelopmentsinthisarena,includingM-spacetheory,offset-solidstheory,andvirtualbound-
aries,havebeendiscussedbySrinivasanandWood(1992),Vasseuretal.(1993),Woodetal.(1993),
andZhangandHuq(1995),wholinkedprocessvariationwithinspectionmethods.