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INT 506/706: Total Quality Management

INT 506/706: Total Quality Management. Introduction to Design of Experiments. Outline. DOE – What is it? Trial and error experiments Definitions Steps in designed experiments Experimental designs. DOE.

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INT 506/706: Total Quality Management

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  1. INT 506/706: Total Quality Management Introduction to Design of Experiments

  2. Outline • DOE – What is it? • Trial and error experiments • Definitions • Steps in designed experiments • Experimental designs

  3. DOE A method of experimenting with the complex interactions among parameters in a process or product with the objective of optimizing the process or product

  4. Trial and error experiments Involves making an educated guess about what should be done to effect change in process or system

  5. Trial and error experiments Example:

  6. Trial and error experiments

  7. Definitions Factor The variable the experimenter will vary in order to determine its effect on a response variable

  8. Definitions Level The value chosen for the experiment and assigned to change the factor Gas example Tire Pressure – Level 1: 28 psi; Level 2: 35 psi

  9. Definitions Controllable Factor Ability to establish and maintain level throughout experiment

  10. Definitions Effect Result or outcome of the experiment

  11. Definitions Response Variable The quality characteristic under study, the variable we want to have an effect on

  12. Definitions Degrees of Freedom The number of independent data points in the samples determines the available degrees of freedom

  13. Definitions Degrees of Freedom • We earn a degree of freedom for every data point we collect • We spend a degree of freedom for each parameter we estimate

  14. Definitions Degrees of Freedom dfTotal = N – 1 = # of observations – 1 dfFactor = L – 1 = # of levels – 1 dfInteraction = dfFactorA * dfFactorB dfError = dfTotal – dfEverythingElse

  15. Definitions Interaction Two or more factors that together produce a result different than what the result of their separate effects would be

  16. Definitions Noise Factor An uncontrollable, but measurable, source of variation in the functional characteristics of a product or process

  17. Definitions Treatment The specific combination of levels for each factor used for a particular run

  18. Definitions Run An experimental trial, the application of one treatment

  19. Definitions Replicate A repeat of a treatment condition

  20. Definitions Repetition Multiple runs of a particular treatment combination/setup

  21. Definitions Significance Used to indicate whether a factor or factor combination caused a significant change in the response variable

  22. Example Factors Material Supplier Press Tonnage 3 levels of each factor SupplierPress Tonnage A 20 B 25 C 30

  23. Example Treatments – 3 x 3 SupplierPress Tonnage A 20 A 25 A 30 B 20 B 25 B 30 C 20 C 25 C 30

  24. Steps in planned experiments • What are you investigating • What is the objective • What are you hoping to learn • What are the critical factors • Which factors can be controlled • What resources will be used

  25. Step 1 Establish the purpose by defining the problem

  26. Step 2 Identify the components of the experiment

  27. Step 3 Design the experiment

  28. Step 4 Perform the experiment

  29. Step 5 Analyze the data

  30. Step 6 Act on the results

  31. Experimental Designs OFAT or Single Factor Experiments Allows for manipulation of only one factor during an experiment

  32. Experimental Designs Full Factorial Designs Consists of all possible combinations of all selected levels of the factors to be investigated To determine # of combinations or runs: LevelsFactors

  33. Experimental Designs Determine # of combinations: 6 Factors at 2 levels = 26 or 64 combinations 4 factors, 2 with 2 levels and 2 with 3 levels = 22 x 32 = 36 treatment combinations

  34. Experimental Designs Full Factorials allows the most complete analysis because it can determine: • Main effects of factors • Effects of factor interactions

  35. Variability 3 Sources of variability contributing to the variability in the numbers • Var. due to conditions of interest (we expect a change from manipulating some factor) • Var. due to measurement process (UNWANTED – errors in measuring equipment or technique) • Var. in experimental material (UNWANTED – trying to make material, or subjects, as similar as possible – block into groups)

  36. Variability 3 types of variability • PLANNED, SYSTEMATIC – due to conditions of interest • CHANCE-LIKE VARIATION – background noise, an unplanned component from the measurement process • UNPLANNED, SYSTEMATIC – Biased, one of the main causes of wrong conclusions and ruined studies • Blocking: turns possible bias into planned, systematic variation • Randomization: turns bias into planned, chance like variation

  37. Variability 3 Basic Principles • Random Assignment • Blocking • Factorial Crossing 1 and 2 are How we collect data 3 is how we construct treatments

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