The Resource Design of Experiments in Production Engineering
Design of Experiments in Production Engineering
- Language
- eng
- Extent
- 1 online resource (201 pages)
- Contents
-
- Preface -- Contents -- Nomenclature -- 1 Screening (Sieve) Design of Experiments in Metal Cutting -- 1 Introduction -- 2 Basic Terminology -- 3 Factor Interactions -- 4 Examples of Variable Interaction in Metal Cutting Testing -- 5 Need for a Screening Test -- 6 Resolution Level -- 7 Using Fractional Factorial DOEs for Factors Screening -- 7.1 Short Overview of Common Fractional Factorial Methods -- 7.1.1 Plackett--Burman DOE -- 7.1.2 Latin Squares -- 7.1.3 Taguchi Method -- 7.2 Two-Stage DOE in Metal Cutting Tests -- 8 The Use of Plackett and Burman DOE as a Sieve DOE in Metal Cutting -- References -- 2 Modelling and Optimization of Machining with the Use of Statistical Methods and Soft Computing -- Abstract -- 1 Introduction -- 2 Factorial Design Method -- 2.1 Description of Factorial Design Method -- 2.2 Applications of Factorial Design Method in Machining -- 3 Taguchi Method -- 3.1 Description of the Method -- 3.2 Application of Taguchi Method in Machining -- 4 Response Surface Methodology -- 4.1 Description of Response Surface Methodology -- 4.2 Application of RSM to Machining -- 5 Analysis of Variance -- 5.1 Application of ANOVA to Machining Problems -- 6 Grey Relational Analysis -- 6.1 Presentation of the Method -- 6.2 Application of GRA to Machining Problems -- 7 Statistical Regression Methods -- 7.1 Applications of Statistical Regression Methods in Machining -- 8 Artificial Neural Networks -- 8.1 Description of Artificial Neural Networks -- 8.2 Applications of ANN in Machining -- 9 Fuzzy Logic -- 9.1 Description of Fuzzy Logic Method -- 9.2 Applications of Fuzzy Logic Method in Machining -- 10 Other Optimization Techniques -- 10.1 Genetic Algorithms -- 10.2 Applications of Genetic Algorithms in Machining -- 10.3 Other Stochastic Algorithms -- 11 A Case Study -- 11.1 Definition of the Input Variables and the Output Responses
- 11.2 DOE and Response Data Implementation -- 11.3 Analysis of Results and Diagnostics of the Statistical Properties of the Model -- 11.4 Final Equations and Models Graphs -- References -- 3 Design of Experiments---Statistical and Artificial Intelligence Analysis for the Improvement of Machining Processes: A Review -- Abstract -- 1 Introduction -- 2 Design of Experiments (DoE) -- 2.1 Classical DoE -- 2.1.1 Multiple Comparisons Methods -- 2.2 Response Surface Methodology (RSM) -- 2.3 Taguchi -- 2.4 Other -- 3 Artificial Intelligence Analysis (AI) -- 3.1 Fuzzy Logic (FL) -- 3.2 Artificial Neural Network (ANN) -- 3.3 Adaptive Neuro-Fuzzy Inference System (ANFIS) -- 3.4 Bayesian Networks (BN) -- 3.5 Genetic Algorithms (GA) -- 4 Modelling and Optimisation for Machining Process -- 5 Conclusions -- Acknowledgment -- References -- 4 A Systematic Approach to Design of Experiments in Waterjet Machining of High Performance Ceramics -- Abstract -- 1 Statistics for Innovation: Design of Experiments -- 1.1 Pre-design and Guidelines for Designing Experiments -- 1.2 Pre-experimental Planning -- 2 Technological Context: Waterjet Machining -- 2.1 Injection Principle -- 2.2 Water Abrasive Finejet Machining -- 2.3 Field of Application -- 2.3.1 Cutting -- 2.3.2 Surface Structuring -- 3 Experimental Equipment -- 3.1 Equipment -- 3.2 Challenges of Data Recording -- 4 Set-up, Design and Testing Phase -- 4.1 Machine Set-up -- 4.2 Design of Experiments -- 5 Analysis of Results and Technological Interpretation -- 5.1 Analysis of Variance -- 5.2 Statistical Results -- 5.3 Technological Interpretation -- 6 Conclusion and Remarks -- Acknowledgments -- References -- 5 Response Surface Modeling of Fractal Dimension in WEDM -- Abstract -- 1 Introduction -- 2 Fractal Dimension as Surface Roughness Parameter -- 3 Roughness Study in WEDM -- 4 Design of Experiments
- 5 Response Surface Methodology -- 6 Experimental Details -- 6.1 Machine Used -- 6.2 Selection of Process Parameters -- 6.3 Workpiece Material -- 6.4 Selection of Design of Experiments -- 6.5 Fractal Dimension Measurement -- 7 Results and Discussion -- 8 Conclusion -- References -- 6 Thrust Force and Torque Mathematical Models in Drilling of Al7075 Using the Response Surface Methodology -- Abstract -- 1 Introduction -- 2 Review of Literature -- 3 Experimental Work -- 4 Proposed Mathematical Models for Thrust Force and Torque -- 5 Conclusions -- Acknowledgments -- References -- 7 Design of Experiments in Titanium Metal Cutting Research -- Abstract -- 1 Introduction -- 2 Experimental Details -- 2.1 Material Details -- 2.2 Experimental Setup Details -- 2.3 Experimental Design -- 2.3.1 Comprehending Objective Function -- 2.3.2 Ordering of the Cutting Parameters and Their Levels -- 2.3.3 Choice of a Suitable Orthogonal Array (OA) -- 2.3.4 Carrying Out Experiments and Data Analysis for Determination of the Optimal Levels -- 3 Results and Discussion -- 3.1 ANOVA -- 3.2 S/N Ratios and Means Evaluation for Optimal Design -- 3.3 Optimum Quality Characteristics Approximation -- 4 Significance of the Study -- Acknowledgement -- References -- 8 Parametric Optimization of Submerged Arc Welding Using Taguchi Method -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Submerged Arc Welding -- 4 Taguchi's Design Method -- 5 Process Parameter Levels -- 6 L9 Orthogonal Array -- 7 Signal-to-Noise Ratio -- 8 ANOVA -- 9 Confirmation Test -- 10 Conclusion -- References -- Index
- Isbn
- 9783319238388
- Label
- Design of Experiments in Production Engineering
- Title
- Design of Experiments in Production Engineering
- Language
- eng
- Cataloging source
- MiAaPQ
- Literary form
- non fiction
- Nature of contents
- dictionaries
- Series statement
- Management and Industrial Engineering Ser
- Label
- Design of Experiments in Production Engineering
- Link
- http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4084467
- Carrier category
- online resource
- Carrier category code
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type code
- txt
- Content type MARC source
- rdacontent
- Contents
-
- Preface -- Contents -- Nomenclature -- 1 Screening (Sieve) Design of Experiments in Metal Cutting -- 1 Introduction -- 2 Basic Terminology -- 3 Factor Interactions -- 4 Examples of Variable Interaction in Metal Cutting Testing -- 5 Need for a Screening Test -- 6 Resolution Level -- 7 Using Fractional Factorial DOEs for Factors Screening -- 7.1 Short Overview of Common Fractional Factorial Methods -- 7.1.1 Plackett--Burman DOE -- 7.1.2 Latin Squares -- 7.1.3 Taguchi Method -- 7.2 Two-Stage DOE in Metal Cutting Tests -- 8 The Use of Plackett and Burman DOE as a Sieve DOE in Metal Cutting -- References -- 2 Modelling and Optimization of Machining with the Use of Statistical Methods and Soft Computing -- Abstract -- 1 Introduction -- 2 Factorial Design Method -- 2.1 Description of Factorial Design Method -- 2.2 Applications of Factorial Design Method in Machining -- 3 Taguchi Method -- 3.1 Description of the Method -- 3.2 Application of Taguchi Method in Machining -- 4 Response Surface Methodology -- 4.1 Description of Response Surface Methodology -- 4.2 Application of RSM to Machining -- 5 Analysis of Variance -- 5.1 Application of ANOVA to Machining Problems -- 6 Grey Relational Analysis -- 6.1 Presentation of the Method -- 6.2 Application of GRA to Machining Problems -- 7 Statistical Regression Methods -- 7.1 Applications of Statistical Regression Methods in Machining -- 8 Artificial Neural Networks -- 8.1 Description of Artificial Neural Networks -- 8.2 Applications of ANN in Machining -- 9 Fuzzy Logic -- 9.1 Description of Fuzzy Logic Method -- 9.2 Applications of Fuzzy Logic Method in Machining -- 10 Other Optimization Techniques -- 10.1 Genetic Algorithms -- 10.2 Applications of Genetic Algorithms in Machining -- 10.3 Other Stochastic Algorithms -- 11 A Case Study -- 11.1 Definition of the Input Variables and the Output Responses
- 11.2 DOE and Response Data Implementation -- 11.3 Analysis of Results and Diagnostics of the Statistical Properties of the Model -- 11.4 Final Equations and Models Graphs -- References -- 3 Design of Experiments---Statistical and Artificial Intelligence Analysis for the Improvement of Machining Processes: A Review -- Abstract -- 1 Introduction -- 2 Design of Experiments (DoE) -- 2.1 Classical DoE -- 2.1.1 Multiple Comparisons Methods -- 2.2 Response Surface Methodology (RSM) -- 2.3 Taguchi -- 2.4 Other -- 3 Artificial Intelligence Analysis (AI) -- 3.1 Fuzzy Logic (FL) -- 3.2 Artificial Neural Network (ANN) -- 3.3 Adaptive Neuro-Fuzzy Inference System (ANFIS) -- 3.4 Bayesian Networks (BN) -- 3.5 Genetic Algorithms (GA) -- 4 Modelling and Optimisation for Machining Process -- 5 Conclusions -- Acknowledgment -- References -- 4 A Systematic Approach to Design of Experiments in Waterjet Machining of High Performance Ceramics -- Abstract -- 1 Statistics for Innovation: Design of Experiments -- 1.1 Pre-design and Guidelines for Designing Experiments -- 1.2 Pre-experimental Planning -- 2 Technological Context: Waterjet Machining -- 2.1 Injection Principle -- 2.2 Water Abrasive Finejet Machining -- 2.3 Field of Application -- 2.3.1 Cutting -- 2.3.2 Surface Structuring -- 3 Experimental Equipment -- 3.1 Equipment -- 3.2 Challenges of Data Recording -- 4 Set-up, Design and Testing Phase -- 4.1 Machine Set-up -- 4.2 Design of Experiments -- 5 Analysis of Results and Technological Interpretation -- 5.1 Analysis of Variance -- 5.2 Statistical Results -- 5.3 Technological Interpretation -- 6 Conclusion and Remarks -- Acknowledgments -- References -- 5 Response Surface Modeling of Fractal Dimension in WEDM -- Abstract -- 1 Introduction -- 2 Fractal Dimension as Surface Roughness Parameter -- 3 Roughness Study in WEDM -- 4 Design of Experiments
- 5 Response Surface Methodology -- 6 Experimental Details -- 6.1 Machine Used -- 6.2 Selection of Process Parameters -- 6.3 Workpiece Material -- 6.4 Selection of Design of Experiments -- 6.5 Fractal Dimension Measurement -- 7 Results and Discussion -- 8 Conclusion -- References -- 6 Thrust Force and Torque Mathematical Models in Drilling of Al7075 Using the Response Surface Methodology -- Abstract -- 1 Introduction -- 2 Review of Literature -- 3 Experimental Work -- 4 Proposed Mathematical Models for Thrust Force and Torque -- 5 Conclusions -- Acknowledgments -- References -- 7 Design of Experiments in Titanium Metal Cutting Research -- Abstract -- 1 Introduction -- 2 Experimental Details -- 2.1 Material Details -- 2.2 Experimental Setup Details -- 2.3 Experimental Design -- 2.3.1 Comprehending Objective Function -- 2.3.2 Ordering of the Cutting Parameters and Their Levels -- 2.3.3 Choice of a Suitable Orthogonal Array (OA) -- 2.3.4 Carrying Out Experiments and Data Analysis for Determination of the Optimal Levels -- 3 Results and Discussion -- 3.1 ANOVA -- 3.2 S/N Ratios and Means Evaluation for Optimal Design -- 3.3 Optimum Quality Characteristics Approximation -- 4 Significance of the Study -- Acknowledgement -- References -- 8 Parametric Optimization of Submerged Arc Welding Using Taguchi Method -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Submerged Arc Welding -- 4 Taguchi's Design Method -- 5 Process Parameter Levels -- 6 L9 Orthogonal Array -- 7 Signal-to-Noise Ratio -- 8 ANOVA -- 9 Confirmation Test -- 10 Conclusion -- References -- Index
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- Extent
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- Form of item
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- Isbn
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