Davim Design of Experiments in Production Engineering

1. Auflage 2016
ISBN: 978-3-319-23838-8
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 201 Seiten

Reihe: Management and Industrial Engineering

This book covers design of experiments (DoE) applied in production engineering as a combination of manufacturing technology with applied management science. It presents recent research advances and applications of design experiments in production engineering and the chapters cover metal cutting tools, soft computing for modelling and optmization of machining, waterjet machining of high performance ceramics, among others.

Autoren/Hrsg.

Davim, J. Paulo

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Nomenclature;9
4;1 Screening (Sieve) Design of Experiments in Metal Cutting;10
4.1;1 Introduction;10
4.2;2 Basic Terminology;12
4.3;3 Factor Interactions;14
4.4;4 Examples of Variable Interaction in Metal Cutting Testing;15
4.5;5 Need for a Screening Test;21
4.6;6 Resolution Level;22
4.7;7 Using Fractional Factorial DOEs for Factors Screening;23
4.7.1;7.1 Short Overview of Common Fractional Factorial Methods;25
4.7.1.1;7.1.1 Plackett--Burman DOE;27
4.7.1.2;7.1.2 Latin Squares;27
4.7.1.3;7.1.3 Taguchi Method;29
4.7.2;7.2 Two-Stage DOE in Metal Cutting Tests;31
4.8;8 The Use of Plackett and Burman DOE as a Sieve DOE in Metal Cutting;31
4.9;References;44
5;2 Modelling and Optimization of Machining with the Use of Statistical Methods and Soft Computing;47
5.1;Abstract;47
5.2;1 Introduction;47
5.3;2 Factorial Design Method;48
5.3.1;2.1 Description of Factorial Design Method;49
5.3.2;2.2 Applications of Factorial Design Method in Machining;51
5.4;3 Taguchi Method;51
5.4.1;3.1 Description of the Method;52
5.4.2;3.2 Application of Taguchi Method in Machining;55
5.5;4 Response Surface Methodology;55
5.5.1;4.1 Description of Response Surface Methodology;56
5.5.2;4.2 Application of RSM to Machining;59
5.6;5 Analysis of Variance;59
5.6.1;5.1 Application of ANOVA to Machining Problems;60
5.7;6 Grey Relational Analysis;61
5.7.1;6.1 Presentation of the Method;61
5.7.2;6.2 Application of GRA to Machining Problems;63
5.8;7 Statistical Regression Methods;63
5.8.1;7.1 Applications of Statistical Regression Methods in Machining;66
5.9;8 Artificial Neural Networks;66
5.9.1;8.1 Description of Artificial Neural Networks;66
5.9.2;8.2 Applications of ANN in Machining;69
5.10;9 Fuzzy Logic;69
5.10.1;9.1 Description of Fuzzy Logic Method;70
5.10.2;9.2 Applications of Fuzzy Logic Method in Machining;71
5.11;10 Other Optimization Techniques;72
5.11.1;10.1 Genetic Algorithms;73
5.11.2;10.2 Applications of Genetic Algorithms in Machining;73
5.11.3;10.3 Other Stochastic Algorithms;74
5.12;11 A Case Study;74
5.12.1;11.1 Definition of the Input Variables and the Output Responses;75
5.12.2;11.2 DOE and Response Data Implementation;75
5.12.3;11.3 Analysis of Results and Diagnostics of the Statistical Properties of the Model;77
5.12.4;11.4 Final Equations and Models Graphs;82
5.13;References;85
6;3 Design of Experiments---Statistical and Artificial Intelligence Analysis for the Improvement of Machining Processes: A Review;97
6.1;Abstract;97
6.2;1 Introduction;98
6.3;2 Design of Experiments (DoE);99
6.3.1;2.1 Classical DoE;100
6.3.1.1;2.1.1 Multiple Comparisons Methods;102
6.3.2;2.2 Response Surface Methodology (RSM);102
6.3.3;2.3 Taguchi;103
6.3.4;2.4 Other;104
6.4;3 Artificial Intelligence Analysis (AI);104
6.4.1;3.1 Fuzzy Logic (FL);105
6.4.2;3.2 Artificial Neural Network (ANN);106
6.4.3;3.3 Adaptive Neuro-Fuzzy Inference System (ANFIS);107
6.4.4;3.4 Bayesian Networks (BN);108
6.4.5;3.5 Genetic Algorithms (GA);108
6.5;4 Modelling and Optimisation for Machining Process;109
6.6;5 Conclusions;110
6.7;Acknowledgment;111
6.8;References;111
7;4 A Systematic Approach to Design of Experiments in Waterjet Machining of High Performance Ceramics;116
7.1;Abstract;116
7.2;1 Statistics for Innovation: Design of Experiments;116
7.2.1;1.1 Pre-design and Guidelines for Designing Experiments;118
7.2.2;1.2 Pre-experimental Planning;118
7.3;2 Technological Context: Waterjet Machining;120
7.3.1;2.1 Injection Principle;121
7.3.2;2.2 Water Abrasive Finejet Machining;122
7.3.3;2.3 Field of Application;123
7.3.3.1;2.3.1 Cutting;123
7.3.3.2;2.3.2 Surface Structuring;124
7.4;3 Experimental Equipment;125
7.4.1;3.1 Equipment;125
7.4.2;3.2 Challenges of Data Recording;125
7.5;4 Set-up, Design and Testing Phase;126
7.5.1;4.1 Machine Set-up;126
7.5.2;4.2 Design of Experiments;130
7.6;5 Analysis of Results and Technological Interpretation;133
7.6.1;5.1 Analysis of Variance;133
7.6.2;5.2 Statistical Results;133
7.6.3;5.3 Technological Interpretation;135
7.7;6 Conclusion and Remarks;139
7.8;Acknowledgments;139
7.9;References;139
8;5 Response Surface Modeling of Fractal Dimension in WEDM;141
8.1;Abstract;141
8.2;1 Introduction;141
8.3;2 Fractal Dimension as Surface Roughness Parameter;142
8.4;3 Roughness Study in WEDM;144
8.5;4 Design of Experiments;144
8.6;5 Response Surface Methodology;145
8.7;6 Experimental Details;146
8.7.1;6.1 Machine Used;146
8.7.2;6.2 Selection of Process Parameters;147
8.7.3;6.3 Workpiece Material;147
8.7.4;6.4 Selection of Design of Experiments;148
8.7.5;6.5 Fractal Dimension Measurement;148
8.8;7 Results and Discussion;148
8.9;8 Conclusion;152
8.10;References;152
9;6 Thrust Force and Torque Mathematical Models in Drilling of Al7075 Using the Response Surface Methodology;156
9.1;Abstract;156
9.2;1 Introduction;156
9.3;2 Review of Literature;157
9.4;3 Experimental Work;159
9.5;4 Proposed Mathematical Models for Thrust Force and Torque;162
9.6;5 Conclusions;168
9.7;Acknowledgments;168
9.8;References;168
10;7 Design of Experiments in Titanium Metal Cutting Research;170
10.1;Abstract;170
10.2;1 Introduction;170
10.3;2 Experimental Details;172
10.3.1;2.1 Material Details;172
10.3.2;2.2 Experimental Setup Details;172
10.3.3;2.3 Experimental Design;175
10.3.3.1;2.3.1 Comprehending Objective Function;175
10.3.3.2;2.3.2 Ordering of the Cutting Parameters and Their Levels;176
10.3.3.3;2.3.3 Choice of a Suitable Orthogonal Array (OA);176
10.3.3.4;2.3.4 Carrying Out Experiments and Data Analysis for Determination of the Optimal Levels;176
10.4;3 Results and Discussion;178
10.4.1;3.1 ANOVA;178
10.4.2;3.2 S/N Ratios and Means Evaluation for Optimal Design;181
10.4.3;3.3 Optimum Quality Characteristics Approximation;184
10.5;4 Significance of the Study;185
10.6;Acknowledgement;186
10.7;References;186
11;8 Parametric Optimization of Submerged Arc Welding Using Taguchi Method;188
11.1;Abstract;188
11.2;1 Introduction;188
11.3;2 Literature Review;189
11.4;3 Submerged Arc Welding;190
11.5;4 Taguchi's Design Method;190
11.6;5 Process Parameter Levels;191
11.7;6 L9 Orthogonal Array;191
11.8;7 Signal-to-Noise Ratio;192
11.9;8 ANOVA;195
11.10;9 Confirmation Test;197
11.11;10 Conclusion;197
11.12;References;198
12;Index;200