1. Fractional Factorial Designs (I want acknowledge the teachings on DOE by the subject experts : Mr. Jim Quinlan on Fraction/Full Factorial Designs and Dr. Ranjit K. Roy on Taguchi.)
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8. Confounding (continued) Notice the ABC column of all +1’s; the ABC effect can not be evaluated. Notice the A and BC columns are identical as are B and AC, C and AB. This means that when effects are computed the analysis will give: A effect = BC effect B effect = AC effect C effect = AB effect
9. Confounding (continued) This does not mean that the effects are equal, it means the design cannot separate them. What is being measured is A + BC, B + AC, C + AB, the combined effects. We say A is confounded with BC and write it as: A BC, B AC, C AB. The actual effects being measured is called the Alias pattern.
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14. Minitab Output – Alias Structure Fractional Factorial Design Factors: 3 Base Design: 3, 4 Resolution: III Runs: 4 Replicates: 1 Fraction: 1/2 Blocks: none Center pts (total): 0 Some main effects are confounded with two-way interactions Design Generators: C = AB Alias Structure I + ABC A + BC B + AC C + AB *** NOTE ***
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21. Main Effects Graphs Data 1 - 1 1 - 1 1 - 1 1 - 1 6 6 6 1 5 6 5 1 4 6 Die Temp Nozzle Temp Shot Size Inj. Press Main Effects Plot (data means) for Data
22. Interaction Graphs Nozzle Temp A, B A, D 1 1 - 1 - 1 6 5 6 0 5 5 5 0 Mean Interaction Plot (data means) for Data Die Temp -1 1 1 1 - 1 - 1 7 0 6 0 5 0 I n j . P r e s s Interaction Plot (data means) for Data Mean Die Temp -1 1
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33. L 12 O.A. Can handle up to eleven 2-level factors.
34. L 18 O.A Can handle one 2-level factor, and up to seven 3-level factors.
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Notes de l'éditeur
The purpose of injection molding is not to get part strength. Part strength is determined by raw material. The only thing that injection molding can do is hurt part strength. Therefore, I want to maximize/optimize the function of injection molding …. SHAPE. The strength will be improved when the function is improved. There is a big difference between theoretical experimenters and practical experimenters. Theoretical experimenters don’t care about the output measure, they are interested in all the affects/interactions with the output measure … all theory stuff. Practical experimenters want to optimize a process … they don’t want interactions and they want their output measure to be the function of the process (so that they can optimize it).
Purpose of blocking, so that the block does not affect factor output. I can determine affect of the block and the affect of the factors independently.
Interactions are spread throughout the matrix. Therefore, if a factor is significant it overpowers the interactions. For example, if you look at AB interaction, there are no other factors that confound with it (same with any other interaction).
Ln(s) is actually a better measure than s because the distribution of variance is skewed (cant go below 0). If you use s you can actually have confidence intervals that give you negative s, which you cant get.