Although many books have been written on this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers. Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as through using statistical methods and readers will find the concepts in this book both familiar and easy to understand.
This new edition includes a chapter on the role of DoE within Six Sigma methodology and also shows through the use of simple case studies its importance in the service industry. It is essential reading for engineers and scientists from all disciplines tackling all kinds of manufacturing, product and process quality problems and will be an ideal resource for students of this topic.
Key Features
- Written in non-statistical language, the book is an essential and accessible text for scientists and engineers who want to learn how to use DoE
- Explains why teaching DoE techniques in the improvement phase of Six Sigma is an important part of problem solving methodology
- New edition includes a full chapter on DoE for services as well as case studies illustrating its wider application in the service industry
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
2.3 Degrees of Freedom
2.4 Confounding
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
4.4 Analytical Tools of DOE
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
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
6.3 Example of a 23 Full Factorial Design
6.4 Example of a 24 Full Factorial Design
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
Exercises
References
8. Some Useful and Practical Tips for Making Your Industrial Experiments Successful
8.1 Introduction
Exercises
References
9. Case Studies
9.1 Introduction
9.2 Case Studies
References
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
10.6 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?
11.4 Six Sigma Methodology (DMAIC Methodology)
11.5 DOE and Its Role Within Six Sigma
Exercises
References
