DFSS BB310 Full Factorial DOE.pptVIP

Design for Six Sigma Full Factorial Design of Experiments DFSS BB310 Full Factorial DOE.ppt Purpose Demonstrate how to generate a full factorial design Demonstrate how to create analyze designs in MINITAB? Demonstrate how to look for factor interactions Demonstrate how to evaluate residuals Demonstrate how to set factors for process optimization DFSS Roadmap Select the Proper DOE Design Experimental Design Considerations Why Learn About Full Factorial DOE? A Full Factorial Design of Experiment will: Provide the most response information about Factor main effects Factor interactions When validated, it will allow a process to be optimized What is a Full Factorial DOE A full factorial DOE is a planned set of tests on the response variable(s) (KPOVs) with one or more inputs (factors) (PIVs), each with all combinations of levels evaluated ANOVA will show which factors are significant Regression analysis will provide the coefficients for the prediction equations (for the case where all factors have 2 levels) Mean Standard deviation Residual analysis will show the fit of the model Full Factorial DOE Objectives Learning the most from as few runs as possible.. Identifying which factors affect mean, variation, both, or have no effect Optimizing the factor levels for desired response Validating the results through confirmation Combination of Factors and Levels (1) A process whose output Y is suspected of being influenced by three inputs A, B and C. The SOP ranges on the inputs are: A 15 through 25, by increments of 1 B 200 through 300, by increments of 2 C 1 or 2 A DOE is planned to test all combinations Combinations of Factors and Levels (2) Setting up a matrix for the factors at all possible process setting levels will produce a really large number of tests. The possible levels for each factor are A = 11 B = 51 C = 2 How many combinations are there? 2 x 51 x 11 = ? Linear Response for Factors at Two Levels The team decides, from process knowledge, that the te