Design of Experiments in Production Engineering (eBook)

Preface 6
Contents 8
Nomenclature 9
1 Screening (Sieve) Design of Experiments in Metal Cutting 10
1 Introduction 10
2 Basic Terminology 12
3 Factor Interactions 14
4 Examples of Variable Interaction in Metal Cutting Testing 15
5 Need for a Screening Test 21
6 Resolution Level 22
7 Using Fractional Factorial DOEs for Factors Screening 23
7.1 Short Overview of Common Fractional Factorial Methods 25
7.1.1 Plackett--Burman DOE 27
7.1.2 Latin Squares 27
7.1.3 Taguchi Method 29
7.2 Two-Stage DOE in Metal Cutting Tests 31
8 The Use of Plackett and Burman DOE as a Sieve DOE in Metal Cutting 31
References 44
2 Modelling and Optimization of Machining with the Use of Statistical Methods and Soft Computing 47
Abstract 47
1 Introduction 47
2 Factorial Design Method 48
2.1 Description of Factorial Design Method 49
2.2 Applications of Factorial Design Method in Machining 51
3 Taguchi Method 51
3.1 Description of the Method 52
3.2 Application of Taguchi Method in Machining 55
4 Response Surface Methodology 55
4.1 Description of Response Surface Methodology 56
4.2 Application of RSM to Machining 59
5 Analysis of Variance 59
5.1 Application of ANOVA to Machining Problems 60
6 Grey Relational Analysis 61
6.1 Presentation of the Method 61
6.2 Application of GRA to Machining Problems 63
7 Statistical Regression Methods 63
7.1 Applications of Statistical Regression Methods in Machining 66
8 Artificial Neural Networks 66
8.1 Description of Artificial Neural Networks 66
8.2 Applications of ANN in Machining 69
9 Fuzzy Logic 69
9.1 Description of Fuzzy Logic Method 70
9.2 Applications of Fuzzy Logic Method in Machining 71
10 Other Optimization Techniques 72
10.1 Genetic Algorithms 73
10.2 Applications of Genetic Algorithms in Machining 73
10.3 Other Stochastic Algorithms 74
11 A Case Study 74
11.1 Definition of the Input Variables and the Output Responses 75
11.2 DOE and Response Data Implementation 75
11.3 Analysis of Results and Diagnostics of the Statistical Properties of the Model 77
11.4 Final Equations and Models Graphs 82
References 85
3 Design of Experiments---Statistical and Artificial Intelligence Analysis for the Improvement of Machining Processes: A Review 97
Abstract 97
1 Introduction 98
2 Design of Experiments (DoE) 99
2.1 Classical DoE 100
2.1.1 Multiple Comparisons Methods 102
2.2 Response Surface Methodology (RSM) 102
2.3 Taguchi 103
2.4 Other 104
3 Artificial Intelligence Analysis (AI) 104
3.1 Fuzzy Logic (FL) 105
3.2 Artificial Neural Network (ANN) 106
3.3 Adaptive Neuro-Fuzzy Inference System (ANFIS) 107
3.4 Bayesian Networks (BN) 108
3.5 Genetic Algorithms (GA) 108
4 Modelling and Optimisation for Machining Process 109
5 Conclusions 110
Acknowledgment 111
References 111
4 A Systematic Approach to Design of Experiments in Waterjet Machining of High Performance Ceramics 116
Abstract 116
1 Statistics for Innovation: Design of Experiments 116
1.1 Pre-design and Guidelines for Designing Experiments 118
1.2 Pre-experimental Planning 118
2 Technological Context: Waterjet Machining 120
2.1 Injection Principle 121
2.2 Water Abrasive Finejet Machining 122
2.3 Field of Application 123
2.3.1 Cutting 123
2.3.2 Surface Structuring 124
3 Experimental Equipment 125
3.1 Equipment 125
3.2 Challenges of Data Recording 125
4 Set-up, Design and Testing Phase 126
4.1 Machine Set-up 126
4.2 Design of Experiments 130
5 Analysis of Results and Technological Interpretation 133
5.1 Analysis of Variance 133
5.2 Statistical Results 133
5.3 Technological Interpretation 135
6 Conclusion and Remarks 139
Acknowledgments 139
References 139
5 Response Surface Modeling of Fractal Dimension in WEDM 141
Abstract 141
1 Introduction 141
2 Fractal Dimension as Surface Roughness Parameter 142
3 Roughness Study in WEDM 144
4 Design of Experiments 144
5 Response Surface Methodology 145
6 Experimental Details 146
6.1 Machine Used 146
6.2 Selection of Process Parameters 147
6.3 Workpiece Material 147
6.4 Selection of Design of Experiments 148
6.5 Fractal Dimension Measurement 148
7 Results and Discussion 148
8 Conclusion 152
References 152
6 Thrust Force and Torque Mathematical Models in Drilling of Al7075 Using the Response Surface Methodology 156
Abstract 156
1 Introduction 156
2 Review of Literature 157
3 Experimental Work 159
4 Proposed Mathematical Models for Thrust Force and Torque 162
5 Conclusions 168
Acknowledgments 168
References 168
7 Design of Experiments in Titanium Metal Cutting Research 170
Abstract 170
1 Introduction 170
2 Experimental Details 172
2.1 Material Details 172
2.2 Experimental Setup Details 172
2.3 Experimental Design 175
2.3.1 Comprehending Objective Function 175
2.3.2 Ordering of the Cutting Parameters and Their Levels 176
2.3.3 Choice of a Suitable Orthogonal Array (OA) 176
2.3.4 Carrying Out Experiments and Data Analysis for Determination of the Optimal Levels 176
3 Results and Discussion 178
3.1 ANOVA 178
3.2 S/N Ratios and Means Evaluation for Optimal Design 181
3.3 Optimum Quality Characteristics Approximation 184
4 Significance of the Study 185
Acknowledgement 186
References 186
8 Parametric Optimization of Submerged Arc Welding Using Taguchi Method 188
Abstract 188
1 Introduction 188
2 Literature Review 189
3 Submerged Arc Welding 190
4 Taguchi's Design Method 190
5 Process Parameter Levels 191
6 L9 Orthogonal Array 191
7 Signal-to-Noise Ratio 192
8 ANOVA 195
9 Confirmation Test 197
10 Conclusion 197
References 198
Index 200