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E-raamat: Statistical Design of Experiments with Engineering Applications

  • Formaat: 272 pages
  • Ilmumisaeg: 08-Apr-2005
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781420056310
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  • Formaat: 272 pages
  • Ilmumisaeg: 08-Apr-2005
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781420056310

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In today's high-technology world, with flourishing e-business and intense competition at a global level, the search for the competitive advantage has become a crucial task of corporate executives. Quality, formerly considered a secondary expense, is now universally recognized as a necessary tool. Although many statistical methods are available for determining quality, there has been no guide to easy learning and implementation until now. Filling that gap, Statistical Design of Experiments with Engineering Applications, provides a ready made, quick and easy-to-learn approach for applying design of experiments techniques to problems. The book uses quality as the main theme to explain various design of experiments concepts.

The authors examine the entire product lifecycle and the tools and techniques necessary to measure quality at each stage. They explain topics such as optimization, Taguchi's method, variance reduction, and graphical applications based on statistical techniques. Wherever applicable the book supplies practical rules of thumb, step-wise procedures that allow you to grasp concepts quickly and apply them appropriately, and examples that demonstrate how to apply techniques. Emphasizing the importance of quality to products and services, the authors include concepts from the field of Quality Engineering. Written with an emphasis on application and not on bogging you down with the theoretical underpinnings, the book enables you to solve 80% of design problems without worrying about the derivation of mathematical formulas.
1. Introduction
1(12)
1.1 What Is Experimental Design?
1(1)
1.2 Applications of Experimental Design
2(1)
1.3 Old Philosophy of Quality
3(1)
1.4 New Philosophy of Quality
4(2)
1.5 Robust Design
6(1)
1.6 Experimentation Steps
7(2)
1.7 Goals and Outline of the Design of Experiments Concepts
9(1)
1.8 Problems
9(1)
References
10(3)
2. Designing and Conducting the Experiment
13(42)
2.1 Introduction
13(1)
2.2 One-Factor-at-a-Time Approach
13(3)
2.3 Two-Level Factorial Designs
16(36)
A. Two-Level Full Factorial Designs
17(8)
B. Fractional Factorial Designs of Resolution III
25(6)
C. Plackett-Burman (PB) Designs
31(5)
D. Fractional Factorial Designs of Resolution IV
36(8)
E. Fractional Factorial Designs of Resolution V
44(8)
2.4 Problems
52(1)
References
52(3)
3. Optimization of the Location Parameter
55(26)
3.1 Introduction
55(1)
3.2 Guidelines For Location Optimization
56(1)
3.3 Replicated Experimental Runs
57(10)
A. Maximizing the Location Parameter
57(4)
B. Prediction
61(1)
C. Hit a Target
62(5)
3.4 An Alternative Approach to the Pareto Chart
67(11)
3.5 Problems
78(2)
References
80(1)
4. Minimization of the Dispersion
81(16)
4.1 Introduction
81(2)
4.2 Dispersion Minimization for Replicated Study
83(6)
4.3 Dispersion Minimization for Unreplicated Study
89(7)
4.4 Problems
96(1)
References
96(1)
5. Taguchi's Approach to the Design of Experiments
97(24)
5.1 Introduction
97(1)
5.2 Loss Function
98(1)
5.3 Taguchi Designs
99(2)
5.4 Signal-to-Noise Ratio
101(5)
A. Nominal-Is-the-Best
101(2)
B. Large-Is-the-Best
103(1)
C. Small-Is-the-Best
104(2)
5.5 Applications of Taguchi's Approach to Robust Designs
106(13)
A. Analysis: Large-Is-the-Best
106(3)
B. Analysis: Small-Is-the-Best
109(4)
C. Analysis: Nominal-Is-the-Best
113(6)
5.6 Comments on the Taguchi Method
119(1)
5.7 Problems
119(1)
References
120(1)
6. Statistical Optimization of the Location Parameter
121(22)
6.1 Introduction
121(2)
6.2 Replicated Two-Level Full Factorial Design
123(5)
6.3 Unreplicated Two-Level Full Factorial Design
128(5)
6.4 Two-Level Fractional Factorial Design
133(5)
6.5 Problems
138(3)
References
141(2)
7. Statistical Minimization of the Dispersion Parameter
143(12)
7.1 Introduction
143(1)
7.2 Replicated Study
143(7)
A. Analysis of Variance Techniques
143(6)
B. Normal Probability Plot of Effects
149(1)
7.3 Unreplicated Study
150(4)
7.4 Problems
154(1)
References
154(1)
8. Validity of the Prediction Equation
155(16)
8.1 Introduction
155(1)
8.2 Graphic Analysis
155(8)
8.3 Adjusted Coefficient of Determination
163(1)
8.4 F Test for Lack of Fit
164(3)
8.5 Analysis Recommendation
167(2)
8.6 Problems
169(1)
References
169(2)
9. Three-Level Factorial Designs
171(22)
9.1 Introduction
171(1)
9.2 Three-Level Full Factorial Design
171(3)
9.3 Box-Behnken Designs
174(3)
9.4 Central Composite Designs
177(11)
A. Rotatable Central Composite Design
178(9)
B. Face Centered Central Composite Design
187(1)
9.5 Three-Level Taguchi Designs
188(2)
9.6 Problems
190(1)
References
191(2)
10. Second-Order Analysis 193(22)
10.1 Introduction
193(1)
10.2 Second-Order Model in Matrix Terms
193(2)
10.3 Estimation of the Second-Order Model Parameters
195(1)
10.4 Estimation of the First-Order Model Parameters
196(2)
10.5 Fitting a Second-Order Model
198(3)
10.6 Inferences about Regression Parameters
201(1)
10.7 Confidence Limits for Predicted Values
202(3)
10.8 Validity of the Prediction Equation
205(1)
10.9 Quadratic Optimization
205(7)
A. Stationary Point
206(1)
B. Optimal Point
206(6)
10.10 Problems
212(1)
References
213(2)
Appendices
Appendix 1 Two-Level Fractional Factorial Designs
215(4)
Appendix 2 Plackett-Burman Designs
219(2)
Appendix 3 Taguchi Designs
221(4)
Appendix 4 Standardized Normal Distribution
225(2)
Appendix 5 Percentiles of the t Distribution
227(2)
Appendix 6 Percentiles of the F Distribution
229(10)
Appendix 7 Some Useful Box-Behnken Designs
239(2)
Appendix 8 Matrix Algebra
241(8)
8.1 Matrices
241(2)
8.2 Matrix Addition and Subtraction
243(1)
8.3 Matrix Multiplication
243(1)
8.4 Special Types of Matrices
244(1)
8.5 Inverse of a Matrix
245(4)
Index 249


Rekab, Kamel; Shaikh, Muzaffar