injection molding DOE: minitab 14

 Your problem is a very common one and the design of choice is a split-plot.  You randomly choose a temperature setting (the split) and then randomize your runs within the split. Once you are finished you choose another temperature setting at random and repeat the procedure until all temperature settings have been run. In your case, it your description of the factors so affected suggest you will have to run a split-split plot. A split-split plot is the same idea as a split plot but it is more complex. I don’t know the capabilities of Minitab with respect to such designs.
  An excellent reference for split and split-split plots is:
 Analysis of Messy Data – Volume 1 – Milliken and Johnson
  You should read up on these designs in order to both understand what the computer design is doing for you (assuming it can generate one) and what you need to do to properly run it an analyze the results.

Stan is absolutely correct. Use the Taguchi DOE in your Minitab* 14 and use Hi/Low on your 11 factors. You can chose # of runs (please pay particular heed to Stan’s recommendation to use the first variable as the one which is hardest to change, second variable will be next hardest to change, etc.)
Once you are done, you can analyze using Taguchi’s bigger is better, nominal is better, smaller is better approach. I also learned that once you have the Taguchi DOE results in place, you can convert the design to a custom Response Surface Design.
PB
 

Thomas, would you share what you determined to be the 11 significant variables?
 
Thanks
Tim

Thomas:
 
Three thoughts, one is to make sure you explain what will become the assumptions you use in your final report so you can later go back if you want and analyze the effects of temperature changes.  For example:
–          One hour is assumed to be sufficient for temperature change stabilization.
–          The split plot approach was chosen as it is assumed to sufficiently randomize temperature change stabilization error for our application.
 
My second thought is for you to note that the ideal injection molding response variable would be the viscosity of the injection liquid as it cools.  Since you are not likely measuring that, you should know the correlation of the response to the control which may be different for each of the 11 factors and not necessarily high.
 
My third thought is that from experience, I have found anything temperature related to be extremely significant with injection molding experimentation (including very significant interactions).  Time related variables are only very significant and pressure related variables are mildly significant.  Along these lines, I’ve also found results to be rather linear and obvious after you’ve run a couple experiments such that prediction and or extrapolation becomes more reliable than with other types of processes.           
 
Good luck with it.
Sincerely,
KN – http://www.KimNiles.com