Process Validation and Design of Experiments

1. Process Validation and Design of Experiments The following PowerPoint presentation was presented by Robert Launsby at the MDM Conference in Anaheim, CA on February 12, 2015. For more information regarding Design of Experiments and Process Validation go to: www.launsby.com Launsby Consulting

2. Process Validation and Design of Experiments Launsby Consulting

3. Robert G Launsby • President of Launsby Consulting in Colorado Springs • MS in engineering • Taught thousands about this topic • APPLICATIONS focused • Author of four books • Co-developer of WISDOM software • 10k gold medalist at National Senior Games 2009 www.launsby.com Launsby Consulting

4. Agenda • Enhancing New Product and Process success rate • How to link DOE (design of experiments) with Process Validation • Quick DOE example • How to link DOE analysis and Monte Carlo Analysis to PV activities • A brief example using Statabot Launsby Consulting

5. How To Improve New Product Success Rate • Customer focus (how do we make the customer successful?) • Management leads change, All Levels • Implement a development process and have the discipline to use it • Make data driven decisions, use tools • Metrics to map progress T≠C Launsby Consulting

6. Roadmap From book “Straight Talk on Process Validation” available at Amazon.com Get your design inputs right at system/subsystem/component level early Launsby Consulting

7. Roadmap Launsby Consulting

8. Roadmap Launsby Consulting

9. Roadmap Launsby Consulting

10. In a Nutshell • IQ….equipment setup correct? • OQ…can we make a good part? Can we make good parts at worst case? This is where Design of Experiments supports PV • PQ…can we make many good parts under production conditions? Launsby Consulting

11. What Is A Designed Experiment? • Systematic, controlled changes of the inputs (factors) to a process in order to observe corresponding changes in the outputs (responses). Why do this: 4 times the information with ½ the tests Launsby Consulting

12. What Is A P-diagram? Outputs Inputs PROCESS Y=F(X) Launsby Consulting

13. Engineering Experimental Design • Not a substitute for knowledge of technology • Incorporates current understanding • Physics first • It is all about good scientific understanding (with some math blended in) Launsby Consulting

14. Taken from the text “Engineering Today’s Designed Experiments” available at Amazon.com Steps In Conducting DOE Keys Plan Define Objective, Select Factors, Levels, Responses, etc Select O.A. Automated by software Have a plan, be there Conduct Graphs, statistical analysis, predict responses at best set points Analyze Demonstrate with data the prediction from transfer function is useful Confirm Launsby Consulting

15. An Example Suppose we have processed an enzyme and want to store in a buffered solution. We want to maintain highest activity level while in storage. What are best conditions for NACL and EDTA to achieve this goal? Launsby Consulting

16. Pareto Chart Launsby Consulting

17. Main Effects Plot Launsby Consulting

18. Interaction Plot Launsby Consulting

19. What Is An Interaction? • Refers to synergism between factors relative to a response. • Two factors interact if the influence of one factor is impacted by the level of another factor Launsby Consulting

20. Transfer Function • The equation (algebraic) • It comes from MLR • Three important assumptions • Two levels • O.A • Variables are on orthogonal scale Software packages use MLR to generate transfer function Launsby Consulting

21. MLR Math includes factors (assumes 4 run previous example), and interaction effect Launsby Consulting

22. Contour Plot Launsby Consulting

23. RSM Plot Launsby Consulting

24. Basic Statistical Analysis Launsby Consulting

25. DOE and PV Steps • Select key factors/levels responses (based upon pre-PV characterization studies) • Conduct Orthogonal Array • Perform analysis • Predict best set-points to target response(s) • Confirm above predictions • Using math. model from DOE, conduct MC using worst case for inputs Launsby Consulting

26. DOE and PV Steps (continued) • Plot variation from Monte Carlo analysis • Run process at actual worst case scenario • Ensure MC analysis and actual results at worst case provide equivalent and acceptable potential process capability Launsby Consulting

27. DOE and Monte Carlo Using the Statabot Launsby Consulting

28. Graphical Analysis of Statbot DOE Launsby Consulting

29. Outputs From Prediction Model Set motor A at 40, motor B at 42 to target 20 seconds for response Launsby Consulting

30. Confirmation of DOE at 20 Seconds Great potential capability at predicted settings, but very short term Note: suppose we know batteries power can vary by +/- 5% before software in Statabot shuts down….what is potential impact of this phenomena? Launsby Consulting

31. Monte Carlo Analysis • Using parsimonious equation from DOE • Vary input power by +/-5 in Monte Carlo analysis and plot the output in response This is a simulation of longer term variation Launsby Consulting

32. Recommendations • Software for DOE • Minitab or JMP if you are well versed in statistics…and need package with numerous capabilities • DOE Wisdom if you have little statistics background and just need DOE support • Fimmtech’s NAUTILUS software if you are injection molder Launsby Consulting