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ME 388 – Applied Instrumentation Laboratory Design of Experiments (DOE)

ME 388 – Applied Instrumentation Laboratory Design of Experiments (DOE). Reference (If you can find it). R.D Moen, T.W. Nolan, L.P. Provost, Improving Quality Through Planned Experimentation , McGraw-Hill, 1991

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ME 388 – Applied Instrumentation Laboratory Design of Experiments (DOE)

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  1. ME 388 – Applied Instrumentation Laboratory Design of Experiments (DOE)

  2. Reference (If you can find it) • R.D Moen, T.W. Nolan, L.P. Provost, Improving Quality Through Planned Experimentation, McGraw-Hill, 1991 • D.C. Montgomery, Design and Analysis of Experiments, 5th Edition, Wiley, 2001

  3. Six Sigma (black belt) • System for constant improvement • Use of statistical tools for process analysis, problem solving and improvement • Six Sigma statement by GE: …The central idea behind Six Sigma is that if you can measure how many "defects" you have in a process, you can systematically figure out how to eliminate them and get as close to "zero defects" as possible…

  4. Definition • A powerful “statistics-based” experimental methodology that is used to efficiently determine how multiple independent variables affect dependent variables of a system or process.

  5. A properly executed DOE will… • Provide the most information • With the fewest amount of tests • Compared to a sequential “string-of-pearls” type approach

  6. DOE’s are used for • Screening experiments • To determine Interactions between independent variables • Optimization

  7. Motivation • Knowledge • Optimization • Improvement

  8. Steps to use DOE • Have some technical knowledge of process or system (assumed here) • Have some statistics background (assumed here) • Design experiment (and run) • Analyze data

  9. Terminology • Factor = independent variable • Level = a given value or setting for an independent variable (for example, a 2-level experiment involves testing a high and low value for each independent variable)

  10. 2 level, 2 factor design (22)

  11. 2 level, 3 factor design (23)

  12. 2 level, 4 factor design (24)

  13. Fractional Factorial Designs • Reduce number of test • At the expense of complete data • Rely on reasonable judgments and technical knowledge • Assume triple, quadruple and greater interactions are not significant

  14. 27-3 design

  15. 27-4 design

  16. Analysis • Develop extended test/design matrix • “Effects” are assessed for each variable and combination of variables • Effects plots can also be generated

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