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Apr 1, 2009 8:09 AM
(473 views)

I have a four step process whose process robustness I'm evaluating and I was hoping to do this with a DOE. Here's the basic process:

Input stream--->Step A--->output/input-->Step B-->output/input-->

Step C-->output/input-->Step D--> product!

Step B has three factors affecting the product purity and yield of its in-process output while steps C and D each have four factors affecting the product purity and yield of their in-process output. What I want to do now is correlate changes of these factors to the PRODUCT purity and yield. It seems to me that a fractional factorial DOE is the way to go. What are your thoughts? Also, is there a mechanism that could allow me to correlate analytical data from output/input of one step with the analytical data from the product? We have a debate going with some of our group arguing that the tolerance intervals of a given step output can serve as a predictor of the product quality. I argue that the remaining process steps aren't accounted for in those tolerance intervals and thus limit their ability to predict the final product quality. Your thoughts?

10 REPLIES

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Apr 1, 2009 11:29 AM
(361 views)

> Also, is there a mechanism that could allow me to correlate analytical data from

> output/input of one step with the analytical data from the product?

Let me make sure I understand ... we are no longer talking about a designed experiment, but simply analysis of data collected from this process, right?

In that case, correlation analysis should be able to do this.

> We have a debate going with some of our group arguing that the tolerance

> intervals of a given step output can serve as a predictor of the product quality.

Tolerance intervals are basically a function of the variance at a particular step. Therefore, you

> I argue that the remaining process steps aren't accounted for in those tolerance

> intervals and thus limit their ability to predict the final product quality.

Any single predictor from Steps A, B, C and D necessarily doesn't use information from later or earlier steps, and so their ability to predict may be limited. Or its ability to predict may not be limited. It depends on your process. But, if you know you have a multi-step process, you will be better off building a model that has all of the predictor variables in the model (plus interactions, curvature as appropriate) from all of the steps. You wouldn't normally model on a single predictor variable's effect on final product quality.

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Apr 1, 2009 1:08 PM
(361 views)

1. Goal: determine how robust a process is within the normal operating ranges of its various steps

2. We're limited by time, so mostly interested in main effects with some two-way interactions welcome (aliasing is okay!). We can always go back and dig deeper if we see something important.

3. Curvature is useful. My initial plan was to simply include some centerpoints to get a very rough idea if there's any curvature there. Again, we can expand if something becomes apparent.

4. "Also, is there a mechanism that could allow me to correlate analytical data from output/input of one step with the analytical data from the product?" You know, I hadn't considered correlation analysis! DOH! Great idea, thanks!

5. "Tolerance intervals are basically a function of the variance at a particular step. Therefore, you could use the variance rather than the tolerance intervals as a predictor. I have never seen tolerance intervals used as predictors." Thanks!

6. Ditto on the remaining points!

Did I say thanks? ;-) Thanks! I look forward to paying it forward. *lol* Not to be redundant or repeat myself. *lol*

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Apr 2, 2009 5:05 AM
(361 views)

You could do a fractional factorial with center points as a first experiment, and then follow up with a central composite design experiment using only the "active" factors found in the fractional factorial.

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Apr 2, 2009 6:24 AM
(361 views)

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Apr 7, 2009 10:57 PM
(361 views)

I have had some very good experiences with those designs in similar cases.

Yves

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Apr 10, 2009 5:51 AM
(361 views)

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Dec 21, 2009 7:54 AM
(361 views)

1) once I have determined the interactions and have created my process parameters can I have jmp help me design a study to check that those parameters can consistently produce good product.

2) What if my final test is just a pass/fail? How do I determine appropriate sample sizes using jmp.

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Dec 21, 2009 12:40 PM
(361 views)

>

> 1) once I have determined the interactions and have

> created my process parameters can I have jmp help me

> design a study to check that those parameters can

> consistently produce good product.

>

> 2) What if my final test is just a pass/fail? How do

> I determine appropriate sample sizes using jmp.

1) JMP will design experiments for you, given the requirements that you provide it. It will also statistically analyze the experiments. By "consistently produce good product", I assume you mean you want to optimize the responses somehow, and if a condition exists that "consistently produce

2) I have an older version of JMP, and it will help you find sample sizes in the case of two sample proportions, which is about as close as it will come to finding sample sizes for pass/fail responses in a designed experiment. I don't know if newer versions of JMP will handle this situation exactly.

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Dec 24, 2009 8:49 PM
(361 views)