دانلود کتاب Design of Experiments for Engineers and Scientists

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Front Cover
Design of Experiments for Engineers and Scientists
Copyright Page
Dedication
Contents
About the author
Preface
Acknowledgements
1 Introduction to industrial experimentation
1.1 Introduction
1.2 Some fundamental and practical issues in industrial experimentation
1.3 Statistical thinking and its role within DOE
Exercises
References
2 Fundamentals of design of experiments
2.1 Introduction
2.2 Basic principles of DOE
2.2.1 Randomisation
2.2.2 Replication
2.2.3 Blocking
2.3 Degrees of freedom
2.4 Confounding
2.4.1 Design resolution
2.4.2 Metrology considerations for industrial designed experiments
2.4.3 Measurement system capability
2.4.4 Some tips for the development of a measurement system
2.5 Selection of quality characteristics for industrial experiments
Exercises
References
3 Understanding key interactions in processes
3.1 Introduction
3.2 Alternative method for calculating the two-order interaction effect
3.3 Synergistic interaction versus antagonistic interaction
3.4 Scenario 1
3.5 Scenario 2
3.6 Scenario 3
Exercises
References
4 A systematic methodology for design of experiments
4.1 Introduction
4.2 Barriers in the successful application of DOE
4.3 A practical methodology for DOE
4.3.1 Planning phase
4.3.1.1 Problem recognition and formulation
4.3.1.2 Selection of response or quality characteristic
4.3.1.3 Selection of process variables or design parameters
4.3.1.4 Classification of process variables
4.3.1.5 Determining the levels of process variables
4.3.1.6 List all the interactions of interest
4.3.2 Designing phase
4.3.3 Conducting phase
4.3.4 Analysing phase
4.4 Analytical tools of DOE
4.4.1 Main effects plot
4.4.2 Interactions plots
4.4.3 Cube plots
4.4.4 Pareto plot of factor effects
4.4.5 NPP of factor effects
4.4.6 NPP of residuals
4.4.7 Response surface plots and regression models
4.5 Model building for predicting response function
4.6 Confidence interval for the mean response
4.7 Statistical, technical and sociological dimensions of DOE
4.7.1 Statistical dimension of DOE
4.7.2 Technical dimension of DOE
4.7.3 Sociological and managerial dimensions of DOE
Exercises
References
5 Screening designs
5.1 Introduction
5.2 Geometric and non-geometric P–B designs
Exercises
References
6 Full factorial designs
6.1 Introduction
6.2 Example of a 22 full factorial design
6.2.1 Objective 1: Determination of main/interaction effects that influence mean plating thickness
6.2.2 Objective 2: Determination of main/interaction effects that influence variability in plating thickness
6.2.3 Objective 4: How to achieve a target plating thickness of 120 units?
6.2.3.1 Effect of plating time on plating thickness
6.2.3.2 Interaction effect between plating time and plating solution temperature (AB)
6.3 Example of a 23 full factorial design
6.3.1 Objective 1: To identify the significant main/interaction effects that affect the process yield
6.3.2 Objective 2: To identify the significant main/interaction effects that affect the variability in process yield
6.3.3 Objective 3: What is the optimal process condition?
6.4 Example of a 24 full factorial design
6.4.1 Objective 1: Which of the main/interaction effects affect mean crack length?
6.4.2 Objective 2: Which of the main/interaction effects affect variability in crack length?
6.4.3 Objective 3: What is the optimal process condition to minimise mean crack length?
6.4.4 More examples of FFEs
Exercises
References
7 Fractional factorial designs
7.1 Introduction
7.2 Construction of half-fractional factorial designs
7.3 Example of a 2(7−4) factorial design
7.4 An application of 2-level fractional factorial design
7.4.1 Example of a 2(5−1) factorial design
7.4.2 Objective 1: To identify the factors which influence the mean free height
7.4.3 Objective 2: To identify the factors which affect variability in the free height of leaf springs
7.4.4 How do we select the optimal factor settings to minimise variability in free height?
7.4.5 Another example of a 2(5−1) factorial design
7.4.6 Example of a 2(7−4) factorial design
7.4.7 Another example of a 2(7−4) factorial design
Exercises
References
Further reading
8 Some useful and practical tips for making your industrial experiments successful
8.1 Introduction
8.1.1 Get a clear understanding of the problem
8.1.2 Project selection
8.1.2.1 Management involvement and commitment
8.1.2.2 Return on investment
8.1.2.3 Project scope
8.1.2.4 Time required to complete the project
8.1.2.5 Value to your organisation
8.1.3 Conduct exhaustive and detailed brainstorming sessions
8.1.4 Teamwork and selection of a team for experimentation
8.1.5 Select the continuous measurable quality characteristics or responses for the experiment
8.1.6 Choice of an appropriate ED
8.1.7 Iterative experimentation
8.1.8 Randomise the experimental trial order
8.1.9 Replicate to dampen the effect of noise or uncontrolled variation
8.1.10 Improve the efficiency of experimentation using a blocking strategy
8.1.11 Understanding the confounding pattern of factor effects
8.1.12 Perform confirmatory runs/experiments
Exercises
References
9 Case studies
9.1 Introduction
9.2 Case studies
9.2.1 Optimisation of a radiographic quality welding of cast iron
9.2.1.1 Objective of the experiment
9.2.1.2 Selection of the response function
9.2.1.3 List of factors and interactions of interest for the experiment
9.2.1.4 Levels of parameters and their ranges
9.2.1.5 Choice of design and number of experimental trials
9.2.1.6 Design generators and the confounding structure of the design
9.2.1.7 Uncoded design matrix with response values
9.2.1.8 Analysis and interpretation of results
9.2.1.9 Confirmatory trials
9.2.2 Reducing process variability using experimental design technique
9.2.2.1 Objective of the experiment
9.2.2.2 Selection of the response
9.2.2.3 List of process parameters and their levels
9.2.2.4 Choice of design and number of experimental trials required for the experiment
9.2.2.5 Design generators and resolution
9.2.2.6 Coded and uncoded design matrix with response values
9.2.2.7 Analysis and interpretation of results
9.2.2.8 Determination of optimal settings to minimise variability
9.2.2.9 Confirmation trials
9.2.2.10 Significance of the work
9.2.3 Slashing scrap rate using fractional factorial experiments
9.2.3.1 Nature of the problem
9.2.3.2 Objective of the experiment
9.2.3.3 Selection of the response
9.2.3.4 List of process parameters and their levels
9.2.3.5 Coded design matrix with response values for the experiment
9.2.3.6 Analysis and interpretation of results
9.2.3.7 Confirmation runs
9.2.4 Optimising the time of flight of a paper helicopter
9.2.4.1 Objective of the experiment
9.2.4.2 Description of the experiment
9.2.4.3 Selection of the response
9.2.4.4 List of design parameters and their levels
9.2.4.5 Choice of design and design matrix for the experiment
9.2.4.6 Statistical analysis and interpretation of results
9.2.4.7 Determination of optimal design parameters
9.2.4.8 Predicted model for time of flight
9.2.4.9 Confirmatory runs
9.2.4.10 Significance of the work
9.2.5 Optimising a wire bonding process using DoE
9.2.5.1 Objective of the experiment
9.2.5.2 Description of the experiment
9.2.5.3 Selection of the response
9.2.5.4 Identification of Process Variables for Experimentation
9.2.5.5 Choice of design and experimental layout
9.2.5.6 Statistical analysis and interpretation
9.2.5.7 Model development based on the significant factor/interaction effects
9.2.5.8 Conclusion
9.2.6 Training for DoE using a catapult
9.2.6.1 Objective of the experiment
9.2.6.2 Selection of response
9.2.6.3 List of factors and their levels used for the experiment
9.2.6.4 Choice of design and experimental layout for the experiment
9.2.6.5 Statistical analysis and interpretation of results
9.2.6.6 Determination of optimal factor settings
9.2.6.7 Confirmatory experiment
9.2.6.8 Significance of the work
9.2.7 Optimisation of core tube life using designed experiments
9.2.7.1 Company’s first attempt to experimental approach
9.2.7.2 Company’s second attempt to use designed experiments
9.2.7.3 Choice of experimental layout for the experiment
9.2.7.4 Statistical analysis and interpretation
9.2.7.5 Determination of the optimal process parameter settings
9.2.7.6 Confirmation trials
9.2.7.7 Significance of the study
9.2.8 Optimisation of a spot welding process using DoE
9.2.8.1 Interactions of interest
9.2.8.2 Statistical analysis of experimental results
9.2.8.3 Loss-function analysis for larger-the-better characteristics
9.2.8.4 Significance of the study
9.2.9 DoE applied to a fizz-flop experiment
9.2.9.1 Hypotheses
9.2.9.2 Experimental plan
9.2.9.3 Execution of experiment
9.2.9.4 Data collection, analysis and interpretation
9.2.9.4.1 Data collection
9.2.9.4.2 Analysis of data
9.2.9.4.3 Experimental conclusions
9.2.9.4.4 Key lessons learned
9.2.9.4.5 Significance of the study
9.2.10 DoE applied to a higher education context
9.2.10.1 Significance of the study
9.2.11 DoE applied to a transactional process
9.2.11.1 Data analysis
9.2.12 DoE applied to a banking operation
9.2.13 DoE applied to a transactional process
9.2.13.1 Significance of the study
9.2.14 Design of experiments in understanding and evaluating teaching effectiveness in UK higher education
9.2.14.1 Phase 1: Planning of the experiment
9.2.14.2 Phase 2: Designing the experimental layout
9.2.14.3 Phase 3: Conducting the experiment
9.2.14.4 Phase 4: Analysing the experiment
9.3 Discussion and limitations of the study
References
Further reading
10 Design of experiments and its applications in the service industry
10.1 Introduction to the service industry
10.2 Fundamental differences between the manufacturing and service organisations
10.3 DOE in the service industry: fundamental challenges
10.4 Benefits of DOE in service/non-manufacturing industry
10.5 DOE: case examples from the service industry
10.5.1 Data entry errors
10.5.2 Debt collection
10.5.3 Emergency department performance
10.6 Role of computer simulation models within DOE
Exercises
References
11 Design of experiments and its role within Six Sigma
11.1 What is Six Sigma?
11.2 How Six Sigma is different from other quality improvement initiatives of the past
11.3 Who makes Six Sigma work?
11.3.1 Six Sigma deployment champions
11.4 Six Sigma methodology (DMAIC methodology)
11.4.1 Define phase
11.4.2 Measure phase
11.4.3 Analyse phase
11.4.4 Improve phase
11.4.5 Control phase
11.5 DOE and its role within Six Sigma
Exercises
References
12 Design of Experiments in the service industry: a critical literature review and future research directions
12.1 Introduction
12.2 Methodology
12.3 Key findings
12.3.1 Experimentation environment and number of replications
12.3.2 Design of Experiments strategies and designs
12.3.3 Number of factors, levels and quality characteristics
12.3.4 Critical success factors
12.3.5 Essential skills required for professionals
12.3.6 Key lessons learned from designed experiments
12.4 Discussion and implications
12.5 Limitations and future directions of research
References
13 Design of Experiments in the service industry: results from a global survey and directions for further research
13.1 Introduction
13.1.1 Research methodology
13.1.1.1 Development of survey instrument and piloting the instrument
13.1.1.2 Sampling strategy and data collection
13.1.2 Key findings
13.1.2.1 Demographical information
13.1.2.2 Education, training and experience in Design of Experiments
13.1.2.3 Challenges in applying Design of Experiments in the service industry
13.1.2.4 Critical success factors for applying Design of Experiments in the service industry
13.1.2.5 Essential skills for successful application of Design of Experiments in the service industry
13.1.3 Discussion and implications
13.1.4 Limitations and directions for future research
Appendix A Statements related to the challenges in applying Design of Experiments (DoE) in the service industry
References
Index
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