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Full Description
This practical text is an essential source of information for those wanting to know how to deal with the variability that exists in every engineering situation. Using typical engineering data, it presents the basic statistical methods that are relevant, in simple numerical terms. In addition, statistical terminology is translated into basic English. In the past, a lack of communication between engineers and statisticians, coupled with poor practical skills in quality management and statistical engineering, was damaging to products and to the economy. The disastrous consequence of setting tight tolerances without regard to the statistical aspect of process data is demonstrated.
This book offers a solution, bridging the gap between statistical science and engineering technology to ensure that the engineers of today are better equipped to serve the manufacturing industry.
Inside, you will find coverage on:
the nature of variability, describing the use of formulae to pin down sources of variation;
engineering design, research and development, demonstrating the methods that help prevent costly mistakes in the early stages of a new product;
production, discussing the use of control charts, and;
management and training, including directing and controlling the quality function.
The Engineering section of the index identifies the role of engineering technology in the service of industrial quality management. The Statistics section identifies points in the text where statistical terminology is used in an explanatory context.
Engineers working on the design and manufacturing of new products find this book invaluable as it develops a statistical method by which they can anticipate and resolve quality problems before launching into production. This book appeals to students in all areas of engineering and also managers concerned with the quality of manufactured products.
Academic engineers can use this text to teach their students basic practical skills in quality management and statistical engineering, without getting involved in the complex mathematical theory of probability on which statistical science is dependent.
Contents
About the Author vii
Foreword ix
Preface xi
Acknowledgements xiii
1 Nature of Variability 1
2 Basic Statistical Methods 9
2.1 Variance 9
2.2 Divisor 'n' or 'n-1'? 11
2.3 Covariance and Correlation 13
2.4 Normal Distribution 14
2.5 Cumulative Frequency Distributions 18
2.6 Binomial Distribution 20
2.7 Poisson Distribution 25
2.8 Chi-squared Distribution 26
Bibliography 31
3 Production 33
3.1 Sampling Inspection 34
3.2 Control Charts 37
3.3 Cusum Charts 40
3.4 Significance Tests 43
3.5 Analysis of Variance 49
3.6 Linear Regression 52
Bibliography 57
4 Engineering Design 61
4.1 Variance Synthesis 61
4.2 Factors of Safety 68
4.3 Tolerances 69
4.4 The Future 71
Bibliography 72
5 Research and Development 75
5.1 Design of Experiments 76
5.2 Evolutionary Operation 95
5.3 Multiple Regression 96
5.4 More Statistical Methods 111
Bibliography 115
6 Background 119
6.1 Measurement 119
6.2 Statistical Computing 121
Bibliography 127
7 Quality Management 129
7.1 Quality Planning 129
7.2 Quality Organisation 135
7.3 Directing the Quality Function 137
7.4 Controlling the Quality Function 140
7.5 Statistical Engineering 141
Bibliography 142
8 Conclusion 145
Appendix A: Guidelines 147
Appendix B: Recommended Books 151
Appendix C: Periodicals 161
Appendix D: Supplementary Bibliography 165
Appendix E: Statistical Tables 171
Index 173
