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博世官方资料：Design of experiments 实验设计手册
Table of contents:
1. Introduction to the design of experiments.3
1.1. The importance of experiments..........3
1.1.1. Acquiring knowledge through experiments..4
1.1.2. Experiments and trials.....8
1.1.3. Experiments and quality..9
1.2. The role of statistics in experiments...9
1.2.1. The sources of variance..9
1.2.2. The principles of statistical experimental design.....11
1.3. Opportunities and risks in DOE.........12
1.3.1. Advantages, success factors and strengths............12
1.3.2. Limits and dangers........12
1.4. DoE at Bosch.......14
1.4.1. The use of DoE in the product creation process (PCP)........14
1.4.2. Qualifications and points of contact...........15
1.5. Elementary rules for designing and conducting experiments....15
2. Task analysis..............19
2.1. Order, project management.............19
2.2. Analysis of the initial situation..........19
2.3. Determining the test strategy...........19
3. Systems analysis........21
4. Experimental design...23
4.1. Determination of response variables.............23
4.2. Determination of factors.....24
4.3. Determination of the factor range....24
4.4. Selection of the model approach.....25
4.5. Determination of factor levels...........27
4.6. Determination of treatments.............28
4.6.1. Trial-and-error method...28
4.6.2. One-factor-at-a-time (OFAT) method........28
4.6.3. Factorial designs............31
4.7. Determination of the number of replications..32
4.8. Determination of run order and grouping.......33
4.9. Design of test run and evaluation....33
4.10. Design of test equipment and specimens......34
4.11. Estimate of time and expenditure....34
5. Conducting the experiment, documentation.........35
5.1. Physical tests.......35
5.2. Computer experiments.......35
6. Evaluation.....37
6.1. Plausibility check..37
6.2. Determining the model equation......37
6.2.1. Example of single-factor test on 2 levels...37
6.2.2. Example of dual-factor test on 2 levels......38
6.2.3. Tests with k factors on 2 levels....40
6.2.4. Regression analysis.......41
6.3. Validation of the model equation......42
6.3.1. Simple significance rating with 2k designs.42
6.3.2. Validation of regression.43
6.3.3. Validation through analysis of variance.....47
6.4. Graphic representation.......50
6.5. Interpretation of results......52
6.6. Conclusions and further procedure..53
7. Applications..54
7.1. Prediction.............54
7.2. Optimization.........54
7.2.1. Gradient-based methods.............55
7.2.2. Simplex method.............56
7.2.3. Evolutionary algorithms.57
7.3. Robustness and reliability analyses.57
8. Advanced approaches..............59
8.1. Fractional factorial designs..............59
8.1.1. Motivation.........59
8.1.2. Effects and confounding..............59
8.1.3. Recommended procedure...........61
8.2. Designs for non-linear relationships.61
8.2.1. Type 3k designs.............62
8.2.2. Central composite designs..........62
8.2.3. D–optimal designs.........63
8.2.4. Recommended procedure...........64
8.3. Alternative model approaches.........64
8.4. Stochastic experimental designs.....67
8.4.1. Plain Monte Carlo (PMC).............67
8.4.2. Latin hypercube (LHC)...68
8.5. Special experimental designs..........68
8.6. Taguchi experimental designs.........69
8.6.1. Motivation: robust products and processes.............69
8.6.2. Taguchi experimental designs.....69
8.6.3. Evaluation and result.....70
8.7. Shainin® method..71
8.7.1. Motivation: the search for the Red X®.......71
8.7.2. Systematic observation..71
8.7.3. Simple tests.....72
8.7.4. Further tools.....75
9. Appendix.......77
9.1. Fundamental concepts in statistics..77
9.1.1. Data series and their characteristic values..............77
9.1.2. Distributions and their characteristic values............78
9.1.3. t-test, comparISOn of two means..81
9.1.4. Minimum number of random samples.......82
9.1.5. Analysis of variance.......84
9.1.6. Regression analysis.......87
9.2. Software tools......90
9.3. Tables.....91
10. Literature.......96
11. Index.............97 |
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