I am designing an experiment to minimize the number of failures in wood-based materials arising from the application of wood screws. The first part is screening for significant factors and 2-way interactions. I am using a single material type and cross-section (1x1 LVL rails with the screws inserted on a specific face, with the same torque and speed) so my experimental factors are:

  1. Length of the rail (continuous)
  2. Distance of the screw from the end of the rail (x position, continuous)
  3. Distance of the screw from the edge of the rail (y position, continuous)
  4. Screw diameter (4 standard sizes, discrete)
  5. Screw length (3 standard lengths, discrete)

As I see it I have to basic options for response variable:

  1. Failed/Pass (meaning that the screw cracked the piece or not, categorical)
  2. Failure rate (I test the same treatment 10-20 times and input the % of failures)

I am using SAS JMP to aid with the DOE and it provides a 24 treatment table with no repetitions. My question is:

Can I simply run each individual treatment 10 times and calculate the ratio of failed components (0%-100%) and call this my response result? Or does this introduce any bias or statistical errors as the original design contemplates no repetitions and the original response would be simply Fail/Pass?

In the end I would like to provide a predictive model to keep failed parts to a minimum.

  • $\begingroup$ Sorry for my ignorance or lack of knowledge with respect to wood based material. Does LVL stand for Laminated veneer lumber $\endgroup$ – Mahendra Gunawardena Sep 6 at 14:34
  • $\begingroup$ @MahendraGunawardena Yes, that's what LVL means. My apologies, I should have been more explicit. $\endgroup$ – JC ME Sep 7 at 7:17

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