Using 6-Sigma Experimental Design Tools in Product Improvement Testing

1. Using 6-Sigma Experimental Design Toolsin Product Improvement Testing MAESC May 11, 2005 Paul Babin, P.E., William Parker

2. Using 6-Sigma Experimental Design Tools in Product Improvement Testing • What is 6-Sigma? • Experimental Design • Rubber Plug Example • Planning the Test • Results – ANOVA • Engineering Model • Regression Analysis • Synthesis of Models and Experiments

3. What is 6-Sigma? 3.4 ppm 3.4 PPM

4. 6-Sigma Process Improvement Methodology Control the process to assure that important improvements are sustained. Define the key processes that affect customers. Analyze the data, converting it to insightful information. Define Measure Analyze Improve Control EXECUTION Measure the performance of key characteristics. Improve the process to achieve the results desired.

5. Comparing Six Sigma, Lean, TOCref Dave Nave, Quality Progress, March 2002

6. Define Project Selection Matrix Cost of Quality Project Charter Measure Key process output variables Financial Metrics Voice of the Customer Analyze Box Plots, Pareto Charts, Control Charts Scatter plots, Comparison Tests Regression Analysis ANOVA (Analysis of Variance) Improve Key process input variables DOE (Design of Experiments) Full Factorial DOEs 2k Fractional Factorial DOEs Robust Designs Response Surface Methodology Improvement Recommendations Control Process Maps, SOPS, Failure Mode and Effects Analysis, Mistake Proofing Control Plan Change Management 6-SigmaTools by DMAIC Phase ref Implementing 6-Sigma, Breyfogle 2003

7. Experimental Design • Statistical Methods that provide an investigator with a way to overcome the difficulties typically encountered including: • Experimental Error (noise) • Confusion of correlation with causation • Complexity of the effects to be studies Adapted from Box, Hunter, and Hunter 1978

8. Design of Experiments Process Factors Response Experimental Error (Noise)

9. Using DOE in a 6-Sigma Project • Select an appropriate Response Variable • Continuous Variable (ratio level) • Measurable • Identify possible factors and interactions • Select factors and factor levels • Plan the experiment (treatment combinations) • Conduct the experiment • Analyze the Results • Recommend Improvements (or further testing)

10. Example : Plug DOE • Response Variable: • Holding Pressure • Factors (and levels): • Tube Size (5 sizes) • Tube Wall (thick and thin) • Plug Material (old & new) • Temp (high & low) • Full factorial design • 40 treatment combinations (5x2x2x2)

11. Running The Test • Insert Plug and condition for 24 hours • Slowly increase air pressure • Note the pressure at which the plug just starts to move

12. Running the Test

13. ANOVA R2 = 0.74

14. ANOVA R2 = 0.74

15. What does that mean? • Validated Test Method. • Reduced (but not eliminated) noise • Discriminate between important differences • Factors explained 74% of the variation • Temperature not important • No Interactions

16. Engineering Model • Predictive Model • Press Fit Concentric Model • Response: Holding Pressure • Variables: • Coefficient of Friction • Elastic Modulus & Poisson’s Ratio • Amount of Compression • Cross Sectional Area

17. Regression Analysis of Engineering Model : Average Test Pressure Observed vs. Predicted

18. Future Analysis – FEA models

19. Engineering Model Holding Pressure = Non-linear function of Amount of Compression Plug Wall thickness Coefficient of friction Young’s modulus Poisson’s ratio Plug length R2 = 80% (using averages) Factorial Experiment Holding Pressure = b1 * Material + b2 * Size + b3 * Wall + error no temperature effect no interactions between factors R2 = 74% Comparing the Models But where do we get all the parameters to plug in? Experimentation!

20. Engineering Models Describe behavior based on physical properties Provides a precise predicted average value Factorial Experiment Describe experimental variation Determine important factors Validate Engr Model Synthesis • 6-Sigma Product Improvement • Understand and Reduce Variation $$$

21. References: • Breyfogle, Forrest W. III, “Implementing Six Sigma – Smarter Solutions Using Statistical Methods, 2nd Edition”, Wiley, 2003. • Box, George E.P., William G. Hunter, J. Stuart Hunter, “Statistics for Experimenters – An Introduction to Design, Data Analysis, and Model Building”, Wiley, 1978. • Nave, Dave, How to Compare Six Sigma, Lean and the Theory of Constraints. Quality Progress, March 2002. • ASTM D 2990 – Standard Test Methods for Tensile, Compressive, and Flexural Creep and Creep-Rupture of Plastics. • Brewer, Peter C., Jan E. Eighme, Using Six Sigma to Improve the Finance Function, Strategic Finance, May 2005.

22. Questions?

23. Extra Slides

24. Test Methods • ASTM D 2990 – Standard Test Methods for Tensile, Compressive, and Flexural Creep and Creep-Rupture of Plastics. • Viscoelastic Creep • Note 8 – Precision and Bias Attempts to develop a precision and bias statement for these test methods have not been successful…

25. 3 sigma = 99.73%2700 ppm defective

26. 6 sigma = 99.999660%3.4 ppm defective even with shift in mean

27. Define Surveys, CTQ Ranking Pareto Chart, Five Whys Technique Measure Company-Wide definition Guidelines Data Collection Plan & Sheets Sigma Calculation Prioritization Matrix Analyze Process Mapping, Value Added Analysis Bottleneck Analysis, Fishbone Diagram Outside Suggestions Deductive Reasoning and FMEA Pareto Chart, Histogram, Dot Plots, Regression Analysis Discussion, Voting Improve Brainstorming Outside Suggestions Voting Cost/Benefit Analysis Solution Prioritization Matrix Piloting Plan Control Standard Operating Procedures Project Library Control Chart Pareto Chart Balanced Scorecard Mistake Proofing Another 6-SigmaTool list ref Using Six Sigma to Improve the Finance Function, Brewer 2005