文档介绍：5. Process Improvement
Improvement
1. Introduction 2. Assumptions
1. Definition of experimental design 1. Measurement system capable
2. Uses 2. Process stable
3. Steps 3. Simple model
4. Residuals well-behaved
3. Choosing an Experimental Design 4. Analysis of DOE Data
1. Set objectives 1. DOE analysis steps
2. Select process variables and levels 2. Plotting DOE data
3. Select experimental design 3. Modeling DOE data
1. Completely randomized 4. Testing and revising DOE models
designs 5. Interpreting DOE results
2. Randomized block designs 6. Confirming DOE results
3. Full factorial designs 7. DOE examples
4. Fractional factorial designs 1. Full factorial example
5. Plackett-Burman designs 2. Fractional factorial example
6. Response surface designs 3. Response surface example
7. Adding center point runs
8. Improving fractional design
resolution
9. Three-level full factorial
designs
10. Three-level, mixed-level and
fractional factorial designs
(1 of 2) [5/1/2006 10:30:17 AM]
5. Process Improvement
5. Advanced Topics 6. Case Studies
1. When classical designs don't work 1. Eddy current probe sensitivity study
2. Computer-aided designs 2. Sonoluminescent light intensity
1. D-Optimal designs study
2. Repairing a design
3. Optimizing a process
1. Single response case
2. Multiple response case
4. Mixture designs
1. Mixture screening designs
2. Simplex-lattice designs
3. Simplex-centroid designs
4. Constrained mixture designs
5. Treating mixture and process
variables
together
5. Nested variation
6. Taguchi designs
7. John's 3/4 fractional factorial
designs
8. posite designs
9. An EDA approach to experiment
design
7. A Glossary of DOE Terminology 8. References
Click here for a detailed table of contents
(2 of 2) [5/1/2006 10:30:17 AM]
5. Process Improvement
5. Process Improvement - Detailed Table