Experimental Quality

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Format: Hardcover
Pub. Date: 2000-01-01
Publisher(s): Kluwer Academic Pub
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Summary

Improving the quality of products and manufacturing processes at low cost is an economic and technological challenge to industrial engineers and managers alike. In today's business world, the implementation of experimental design techniques often falls short of the mark due to a lack of statistical knowledge on the part of engineers and managers in their analyses of manufacturing process quality problems. This timely book aims to fill this gap in the statistical knowledge required by engineers to solve manufacturing quality problems by using Taguchi experimental design methodology. The book increases awareness of strategic methodology through real-life case studies, providing valuable information for both academics and professionals with no prior knowledge of the theory of probability and statistics. Experimental Quality : Provides a unique framework to help engineers and managers address quality problems and use strategic design methodology. Offers detailed case studies illustrating the implementation of experimental design theory. Is easily accessible without prior knowledge or understanding of probability and statistics. This book provides an excellent resource for both academic and industrial environments, and will prove invaluable to practising industrial engineers, quality engineers and engineering managers from all disciplines.

Author Biography

Dr. Jiju Antony is a research fellow wit the Portsmouth Business School, University of Portsmouth.

Table of Contents

Preface ix
Acknowledgments xi
Introduction
1(16)
Introduction
1(1)
What is design of experiments (DOE)?
2(1)
Why design of experiments or statistically designed experiments?
3(1)
Three approaches to design of experiments---classical, Taguchi and Shainin
4(3)
Classical design of experiments
4(1)
Taguchi methods
5(1)
Shainin methods
6(1)
Benefits of Taguchi DOE in manufacturing
7(6)
Problems and gaps in the state of the art
13(4)
Exercises
15(1)
References
15(2)
The Taguchi approach to quality improvement
17(30)
Taguchi's definition of quality
17(1)
Understanding variation
18(2)
Measures of variation
20(3)
The range, ``R''
20(1)
The standard deviation, ``SD''
21(1)
The mean deviation, ``MD''
22(1)
Variation and its influence on quality
23(1)
Traditional and Taguchi's approach to quality loss functions
24(5)
Determination of manufacturing tolerances
29(2)
Other loss functions
31(4)
Smaller-the-better quality characteristics
31(2)
Larger-the-better quality characteristics
33(2)
An example of Taguchi's loss function analysis
35(2)
Taguchi's seven points of achieving quality
37(2)
Taguchi's quality engineering system
39(8)
On-line quality control system
40(2)
Off-line quality control system
42(3)
Exercises
45(1)
References
45(2)
The Taguchi approach to industrial experimentation
47(26)
Traditional approach to experimentation
47(4)
What are orthogonal arrays?
51(2)
The role of orthogonal arrays
53(3)
Linear graphs
56(7)
Degrees of freedom
63(3)
Randomization in industrial designed experiments
66(1)
Selecting a standard OA for two-level factors
67(6)
Exercises
70(1)
References
71(2)
Assignment of factor and interaction effects to an OA
73(10)
Introduction
73(1)
How to assign factor effects to an OA
74(9)
Exercises
79(2)
References
81(2)
Classification of factors and choice of quality characteristics
83(20)
Classification of factors in Taguchi's experimental design methodology
83(6)
Control factors (x)
83(3)
Noise factors (z)
86(2)
Signal factors (M)
88(1)
The role and contribution of noise factors in industrial experiments
89(1)
Design for robustness---the key to improve product and process quality
90(1)
Treating noise factors incorrectly
91(1)
Taguchi's product array approach to experimentation
92(1)
Choice of quality characteristics for industrial experiments
93(10)
Examples of quality characteristics or responses
95(1)
Multiple quality characteristics or responses
96(4)
Quality characteristics for industrial experiments
100(1)
Exercises
100(1)
References
100(3)
A strategic methodology for Taguchi design of experiments
103(14)
Introduction
103(1)
Devised methodology
104(11)
Comments
115(2)
Exercises
115(1)
References
116(1)
Problem classification
117(18)
Introduction
117(1)
Tools for the development of the problem classification framework
118(3)
Understanding and analysing the process
119(1)
Identification and investigation of the problem
119(1)
Prioritization of problem causes
120(1)
Problem classification
121(1)
Problem classification framework
121(2)
Generic problem source (GPS)
123(3)
Problem selection framework (PSF)
126(6)
Conclusions
132(3)
Exercises
132(1)
References
133(2)
Metrology considerations for industrial experimentation
135(16)
Introduction
135(1)
Method of measurement
136(1)
Direct method of measurement
136(1)
Indirect method of measurement
136(1)
Comparison method of measurement
136(1)
Types of errors in measurements
136(1)
Precision and accuracy
137(1)
Precision
137(1)
Accuracy
137(1)
Properties of a good measurement system
138(1)
The role of measurements in industrial experiments
139(1)
Gauge repeatability and reproducibility
140(1)
Planning gauge R&R studies
141(7)
Procedure for conducting a gauge R&R study
142(1)
Statistical control charts for analysing the measurement process variation
142(3)
Analysis of results from R&R studies
145(3)
Sampling variation in measurement system analysis
148(1)
Environmental considerations for measurements
149(2)
Exercises
150(1)
References
150(1)
Analysis and interpretations of data from Taguchi experiments
151(44)
Introduction
151(1)
Main and interaction effects
152(10)
Determination of the statistical significance of the main and interaction effects
162(10)
Signal-to-Noise ratio (SNR)
172(2)
Relationship between the SNR and quality loss function (QLF)
174(1)
When and how to use the SNR analysis
175(3)
ANOVA for the signal-to-noise ratio
178(2)
Determination of optimal process parameter settings
180(2)
Estimation of the response at the optimal condition
182(2)
Confidence interval for the estimated value
184(2)
Confirmation run or experiment
186(1)
Omega transformation
187(1)
Conclusions
188(7)
Exercises
189(4)
References
193(2)
Industrial case studies
195(30)
Introduction
195(1)
Case studies
196(29)
Optimization of the life of a critical component in a hydraulic valve
196(8)
Optimization of welding on cast iron using Taguchi methods
204(7)
Reducing variability in transformer inductance through Taguchi methods
211(7)
Optimization of machine performance using Taguchi methods
218(7)
Appendices 225(14)
Glossary 239(8)
Index 247

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