Design of Experiments for Engineers and Scientists, Second Edition

275 Pages·2014·6.78 MB·English

by  Jiju Antony

Design of Experiments for Engineers
and Scientists
SECOND EDITION
Jiju Antony
School of Management and Languages, Heriot-Watt University, Edinburgh,
Scotland, UK
Table of Contents
Cover image
Title page
Copyright
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
Copyright
Elsevier
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225 Wyman Street, Waltham, MA 02451, USA
First edition 2003
Second edition 2014
Copyright © 2014, 2003 Jiju Antony. Published by Elsevier Ltd. All rights
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Preface
Design of Experiments (DOE) is a powerful technique used for both exploring
new processes and gaining increased knowledge of existing processes, followed
by optimising these processes for achieving world-class performance. My
involvement in promoting and training in the use of DOE dates back to the mid-
1990s. There are plenty of books available in the market today on this subject
written by classic statisticians, although the majority of them are better suited to
other statisticians than to run-of-the-mill industrial engineers and business
managers with limited mathematical and statistical skills.
DOE never has been a favourite technique for many of today’s engineers and
managers in organisations due to the number crunching involved and the
statistical jargon incorporated into the teaching mode by many statisticians. This
book is targeted to people who have either been intimidated by their attempts to
learn about DOE or who have never appreciated the true potential of DOE for
achieving breakthrough improvements in product quality and process efficiency.
This book gives a solid introduction to the technique through a myriad of
practical examples and case studies. The second edition of the book has
incorporated two new chapters and both cover the latest developments on the
topic of DOE. Readers of this book will develop a sound understanding of the
theory of DOE and practical aspects of how to design, analyse and interpret the
results of a designed experiment. Throughout this book, the emphasis is on the
simple but powerful graphical tools available for data analysis and interpretation.
All of the graphs and figures in this book were created using Minitab version
15.0 for Windows.
I sincerely hope that practising industrial engineers and managers as well as
researchers in academic world will find this book useful in learning how to apply
DOE in their own work environment. The book will also be a useful resource for
people involved in Six Sigma training and projects related to design optimisation
and process performance improvements. In fact, I have personally observed that
the number of applications of DOE in non-manufacturing sectors has increased
significantly because of the methodology taught to Six Sigma professionals such
as Six Sigma Green Belts and Black Belts.
The second edition has a chapter dedicated to DOE for non-manufacturing
processes. As a mechanical engineer, I was not convinced about the application
of DOE in the context of the service industry and public sector organisations
including Higher Education. I have included a simple case study showing the
power of DOE in a university setting. I firmly believe that DOE can be applied
to any industrial setting, although there will be more challenges and barriers in
the non-manufacturing sector compared to traditional manufacturing companies.
I hope that this book inspires readers to get into the habit of applying DOE for
problem-solving and process troubleshooting. I strongly recommend that readers
of this book continue on a more advanced reference to learn about topics which
are not covered here. I am indebted to many contributors and gurus for the
development of various experimental design techniques, especially Sir Ronald
Fisher, Plackett and Burman, Professor George Box, Professor Douglas
Montgomery, Dr Genichi Taguchi and Dr Dorian Shainin.