Coverart for item
The Resource Design of Experiments in Production Engineering

Design of Experiments in Production Engineering

Label
Design of Experiments in Production Engineering
Title
Design of Experiments in Production Engineering
Creator
Subject
Language
eng
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Management and Industrial Engineering Ser
Design of Experiments in Production Engineering
Label
Design of Experiments in Production Engineering
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4084467
Publication
Copyright
Related Contributor
Related Location
Related Agents
Related Authorities
Related Subjects
Related Items
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
http://library.link/vocab/cover_art
https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9783319238388&userID=ebsco-test&password=ebsco-test
Dimensions
unknown
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b4384065'}
Extent
1 online resource (201 pages)
Form of item
online
Isbn
9783319238388
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
unknown sound
Specific material designation
remote

Library Locations

    • Folsom LibraryBorrow it
      110 8th St, Troy, NY, 12180, US
      42.729766 -73.682577
Processing Feedback ...