5. Process Improvement 5. Process Improvement 1. Introduction 2. Assumptions 1. Definition of experimental 1. Measurement system capable design 2. Process stable 2. Uses 3. Simple model 3. Steps 4. Residuals well-behaved 3. Choosing an Experimental 4. Analysis of DOE Data Design 1. DOE analysis steps 1. Set objectives 2. Plotting DOE data 2. Select process variables and 3. Modeling DOE data levels 4. Testing and revising DOE 3. Select experimental design models 1. Completely randomized 5. Interpreting DOE results designs 6. Confirming DOE results 2. Randomized block 7. DOE examples designs 1. Full factorial example 3. Full factorial designs 2. Fractional factorial 4. Fractional factorial example designs 3. Response surface 5. Plackett-Burman example 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 5. Advanced Topics 6. Case Studies 1. When classical designs don't 1. Eddy current probe sensitivity work study 2. Computer-aided designs 2. Sonoluminescent light 1. D-Optimal designs intensity study http://www.itl.nist.gov/div898/handbook/pri/pri.htm[6/27/2012 2:26:20 PM] 5. Process Improvement 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. Small composite designs 9. An EDA approach to experiment design 7. A Glossary of DOE 8. References Terminology Click here for a detailed table of contents http://www.itl.nist.gov/div898/handbook/pri/pri.htm[6/27/2012 2:26:20 PM] 5. Process Improvement 5. Process Improvement - Detailed Table of Contents [5.] 1. Introduction [5.1.] 1. What is experimental design? [5.1.1.] 2. What are the uses of DOE? [5.1.2.] 3. What are the steps of DOE? [5.1.3.] 2. Assumptions [5.2.] 1. Is the measurement system capable? [5.2.1.] 2. Is the process stable? [5.2.2.] 3. Is there a simple model? [5.2.3.] 4. Are the model residuals well-behaved? [5.2.4.] 3. Choosing an experimental design [5.3.] 1. What are the objectives? [5.3.1.] 2. How do you select and scale the process variables? [5.3.2.] 3. How do you select an experimental design? [5.3.3.] 1. Completely randomized designs [5.3.3.1.] 2. Randomized block designs [5.3.3.2.] 1. Latin square and related designs [5.3.3.2.1.] 2. Graeco-Latin square designs [5.3.3.2.2.] 3. Hyper-Graeco-Latin square designs [5.3.3.2.3.] 3. Full factorial designs [5.3.3.3.] 1. Two-level full factorial designs [5.3.3.3.1.] 2. Full factorial example [5.3.3.3.2.] 3. Blocking of full factorial designs [5.3.3.3.3.] 4. Fractional factorial designs [5.3.3.4.] 1. A 23-1 design (half of a 23) [5.3.3.4.1.] 2. Constructing the 23-1 half-fraction design [5.3.3.4.2.] 3. Confounding (also called aliasing) [5.3.3.4.3.] 4. Fractional factorial design specifications and design resolution [5.3.3.4.4.] 5. Use of fractional factorial designs [5.3.3.4.5.] 6. Screening designs [5.3.3.4.6.] 7. Summary tables of useful fractional factorial designs [5.3.3.4.7.] 5. Plackett-Burman designs [5.3.3.5.] 6. Response surface designs [5.3.3.6.] 1. Central Composite Designs (CCD) [5.3.3.6.1.] 2. Box-Behnken designs [5.3.3.6.2.] 3. Comparisons of response surface designs [5.3.3.6.3.] 4. Blocking a response surface design [5.3.3.6.4.] 7. Adding centerpoints [5.3.3.7.] 8. Improving fractional factorial design resolution [5.3.3.8.] 1. Mirror-Image foldover designs [5.3.3.8.1.] http://www.itl.nist.gov/div898/handbook/pri/pri_d.htm[6/27/2012 2:23:22 PM] 5. Process Improvement 2. Alternative foldover designs [5.3.3.8.2.] 9. Three-level full factorial designs [5.3.3.9.] 10. Three-level, mixed-level and fractional factorial designs [5.3.3.10.] 4. Analysis of DOE data [5.4.] 1. What are the steps in a DOE analysis? [5.4.1.] 2. How to "look" at DOE data [5.4.2.] 3. How to model DOE data [5.4.3.] 4. How to test and revise DOE models [5.4.4.] 5. How to interpret DOE results [5.4.5.] 6. How to confirm DOE results (confirmatory runs) [5.4.6.] 7. Examples of DOE's [5.4.7.] 1. Full factorial example [5.4.7.1.] 2. Fractional factorial example [5.4.7.2.] 3. Response surface model example [5.4.7.3.] 5. Advanced topics [5.5.] 1. What if classical designs don't work? [5.5.1.] 2. What is a computer-aided design? [5.5.2.] 1. D-Optimal designs [5.5.2.1.] 2. Repairing a design [5.5.2.2.] 3. How do you optimize a process? [5.5.3.] 1. Single response case [5.5.3.1.] 1. Single response: Path of steepest ascent [5.5.3.1.1.] 2. Single response: Confidence region for search path [5.5.3.1.2.] 3. Single response: Choosing the step length [5.5.3.1.3.] 4. Single response: Optimization when there is adequate quadratic fit [5.5.3.1.4.] 5. Single response: Effect of sampling error on optimal solution [5.5.3.1.5.] 6. Single response: Optimization subject to experimental region constraints [5.5.3.1.6.] 2. Multiple response case [5.5.3.2.] 1. Multiple responses: Path of steepest ascent [5.5.3.2.1.] 2. Multiple responses: The desirability approach [5.5.3.2.2.] 3. Multiple responses: The mathematical programming approach [5.5.3.2.3.] 4. What is a mixture design? [5.5.4.] 1. Mixture screening designs [5.5.4.1.] 2. Simplex-lattice designs [5.5.4.2.] 3. Simplex-centroid designs [5.5.4.3.] 4. Constrained mixture designs [5.5.4.4.] 5. Treating mixture and process variables together [5.5.4.5.] 5. How can I account for nested variation (restricted randomization)? [5.5.5.] 6. What are Taguchi designs? [5.5.6.] 7. What are John's 3/4 fractional factorial designs? [5.5.7.] 8. What are small composite designs? [5.5.8.] 9. An EDA approach to experimental design [5.5.9.] 1. Ordered data plot [5.5.9.1.] 2. DOE scatter plot [5.5.9.2.] 3. DOE mean plot [5.5.9.3.] 4. Interaction effects matrix plot [5.5.9.4.] 5. Block plot [5.5.9.5.] 6. DOE Youden plot [5.5.9.6.] 7. |Effects| plot [5.5.9.7.] 1. Statistical significance [5.5.9.7.1.] 2. Engineering significance [5.5.9.7.2.] 3. Numerical significance [5.5.9.7.3.] http://www.itl.nist.gov/div898/handbook/pri/pri_d.htm[6/27/2012 2:23:22 PM] 5. Process Improvement 4. Pattern significance [5.5.9.7.4.] 8. Half-normal probability plot [5.5.9.8.] 9. Cumulative residual standard deviation plot [5.5.9.9.] 1. Motivation: What is a Model? [5.5.9.9.1.] 2. Motivation: How do we Construct a Goodness-of-fit Metric for a Model? [5.5.9.9.2.] 3. Motivation: How do we Construct a Good Model? [5.5.9.9.3.] 4. Motivation: How do we Know When to Stop Adding Terms? [5.5.9.9.4.] 5. Motivation: What is the Form of the Model? [5.5.9.9.5.] 6. Motivation: Why is the 1/2 in the Model? [5.5.9.9.6.] 7. Motivation: What are the Advantages of the LinearCombinatoric Model? [5.5.9.9.7.] 8. Motivation: How do we use the Model to Generate Predicted Values? [5.5.9.9.8.] 9. Motivation: How do we Use the Model Beyond the Data Domain? [5.5.9.9.9.] 10. Motivation: What is the Best Confirmation Point for Interpolation? [5.5.9.9.10.] 11. Motivation: How do we Use the Model for Interpolation? [5.5.9.9.11.] 12. Motivation: How do we Use the Model for Extrapolation? [5.5.9.9.12.] 10. DOE contour plot [5.5.9.10.] 1. How to Interpret: Axes [5.5.9.10.1.] 2. How to Interpret: Contour Curves [5.5.9.10.2.] 3. How to Interpret: Optimal Response Value [5.5.9.10.3.] 4. How to Interpret: Best Corner [5.5.9.10.4.] 5. How to Interpret: Steepest Ascent/Descent [5.5.9.10.5.] 6. How to Interpret: Optimal Curve [5.5.9.10.6.] 7. How to Interpret: Optimal Setting [5.5.9.10.7.] 6. Case Studies [5.6.] 1. Eddy Current Probe Sensitivity Case Study [5.6.1.] 1. Background and Data [5.6.1.1.] 2. Initial Plots/Main Effects [5.6.1.2.] 3. Interaction Effects [5.6.1.3.] 4. Main and Interaction Effects: Block Plots [5.6.1.4.] 5. Estimate Main and Interaction Effects [5.6.1.5.] 6. Modeling and Prediction Equations [5.6.1.6.] 7. Intermediate Conclusions [5.6.1.7.] 8. Important Factors and Parsimonious Prediction [5.6.1.8.] 9.