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5.

Process Improvement

1. Introduction
  1. Definition of experimental design
  2. Uses
  3. Steps
2. Assumptions
  1. Measurement system capable
  2. Process stable
  3. Simple model
  4. Residuals well-behaved
3. Choosing an Experimental Design
  1. Set objectives
  2. Select process variables and levels
  3. Select experimental design
    1. Completely randomized designs
    2. Randomized block designs
    3. Full factorial designs
    4. Fractional factorial designs
    5. Plackett-Burman designs
    6. Response surface designs
    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
4. Analysis of DOE Data
  1. DOE analysis steps
  2. Plotting DOE data
  3. Modeling DOE data
  4. Testing and revising DOE models
  5. Interpreting DOE results
  6. Confirming DOE results
  7. DOE examples
    1. Full factorial example
    2. Fractional factorial example 
    3. Response surface example
5. Advanced Topics
  1. When classical designs don't work
  2. Computer-aided designs
    1. D-Optimal designs
    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

    6. together
  5. Nested variation
  6. Taguchi designs
  7. John's 3/4 fractional factorial designs
  8. Small composite designs
  9. An EDA approach to experiment design
6. Case Studies
  1. Eddy current probe sensitivity study
  2. Sonoluminescent light intensity study
7. A Glossary of DOE Terminology 8. References
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