Statistical and Computational Techniques in Manufacturing (eBook)

J. Paulo Davim received his PhD degree in Mechanical Engineering from the University of Porto in 1997 and the Aggregation from the University of Coimbra in 2005. Between 1986/96, he was a lecturer at the University of Porto. Currently, he is an Aggregate Professor at the Department of Mechanical Engineering of the University of Aveiro and the Head of MACTRIB - Machining and Tribology Research Grouphttp://www2.mec.ua.pt/machining . He has over 24 years of teaching and research experience in manufacturing, materials and mechanical engineering, with special emphasis in Machining & Tribology. He is the Editor-in-Chief of six international journals, Guest Editor of journals, books Editor, book Series Editor and Scientific Advisory for manyinternational journals and conferences. Presently, he is an Editorial Board member of 15 international journals and reviewer for than 50 prestigious ISI web Science journals. In addition, he has also authored and co-authored more than 20 book chapters and 300 articles in journals and conferences (more 150 articles in ISI Web Science, h-index 16).Personal Web page:http://www2.mec.ua.pt/machining/pers-davim.htmPublications:http://www2.mec.ua.pt/machining/Publications_Mactrib.htm

Title 
1 
Preface 4
Contents 5
Design of Experiment Methods in Manufacturing: 
13 
Introduction 13
Design of Experiment as a Formal Statistical Method 13
Short History 15
What Is This Chapter All about? 
17 
Basic Terminology 17
Response 19
Levels of the Factors 20
Experimental Plan – Factorial Experiments 22
Full Factorial Design 22
Resolution Level 34
More Specialized Designs 34
Orthogonal Array and Taguchi Method 34
Sieve DOE 37
Split-Plot DOE 46
Group Method of Data Handling (GMDH) 56
Strategy and Principal Steps in Using DOE 60
References 
64 
Stream-of-Variation Based Quality Assurance 
67 
Introduction 67
3D Variation Propagation Modeling 70
Fundamentals 70
Definition of Coordinate Systems 74
Derivation of the DMVs 76
Derivation of the SoV Model 90
Process Planning 93
Process Plan Evaluation 94
Process Plan Improvement 95
Case Study 100
Process Plan Evaluation 102
Process Plan Improvement 103
Conclusions 105
References 
109 
Finite Element Modeling of Chip Formation 
112 
Introduction 112
Basics of Machining 114
Orthogonal Cutting Model 115
Cutting Forces 117
Cutting Temperature 120
Chip Morphology 
121 
Basics of FEM 121
Generalized Steps in FEM 122
Modeling Techniques 123
Brief History of FEM in Machining 130
Formulation of Two-Dimensional FE Model for Machining 131
Formulation Steps 131
ABAQUS Platform 
138 
Case Studies in ABAQUS Platform 141
Case Study I: FE Simulation of Continuous Chip 
142 
Case Study II: FE Simulation of Segmented Chip 
145 
References 
151 
GA-Fuzzy Approaches: Application to Modeling 
156 
Introduction 156
Fuzzy Logic 158
Crisp Set and Fuzzy Set 158
Fuzzy Membership Function 159
Fuzzy Set Operators 162
Classical Logical Operations and Fuzzy Logical Operations 162
implication operation 164
Decomposition of Compound Rules 164
Aggregation of Rule 165
Composition Technique of Fuzzy Relation 165
Fuzzy Inferences 166
Fuzzification and De-fuzzification 170
Fuzzy Rule-Based Model 171
Genetic Algorithm 174
Genetic Algorithms and the Traditional Methods 175
Simple Genetic Algorithm 176
Description of Working Principle of GA 
181 
Genetic Fuzzy Approaches 183
Application to Modeling of Machining Process 189
Modeling Power Requirement and Surface Roughness in 
189 
Study of Drilling Performances with Minimum Quantity 
191 
References 
196 
Single and Multi-objective Optimization Methodologies 
197 
Introduction 197
Modeling Machining Optimization 198
Definition of Optimization 198
Objective Functions in Machining Problems 199
Mathematical Modeling of Process Parameters and Objectives 201
Single-Objective and Multi-objective Optimization 202
Choosing Optimization Philosophy 202
Building Meta-Models for Machining Processes Using 
203 
Basics of Artificial Neural Networks (ANNs) 203
Machining Data Sets for Training, Validation and Test 
205 
Quality Characteristics Predictions Using ANN 
206 
Genetic and Evolutionary Algorithms 206
Basic Genetic Algorithm Structure 207
Evolutionary Algorithms 
213 
Variations of Evolutionary Algorithms 217
Particle Swarm Optimization 217
Simulated Annealing 219
Tabu Search 220
Ant-Colony Optimization 221
Tribes 222
Hybrids of Evolutionary Algorithms 
223 
Conclusions 224
References 
225 
Manufacturing Seamless Reservoirs by Tube Forming: 
263 
Introduction 263
Innovative Manufacturing Process 265
Tooling Concept 265
Preforms and Mandrels 267
Lubrication 
269 
Mechanical Testing of Materials 269
Flow Curve 269
Critical Instability Load 270
Theoretical and Experimental Background 271
Finite Element Flow Formulation 271
Friction and Contact 275
Experimental Development 277
Mechanics of the Process 278
Modes of Deformation 278
Formability 279
Forming Load 281
Applications 281
Performance and Feasibility of the Process 282
Requirements for Aerospace Applications 286
Conclusions 289
References 289
Author Index 291