Hi ,
I'd like to design an experiment to investigate the relationship between active material concentration and the final particle size. I want to test 10 different active material suppliers and see how the active material concentration affects the resulting particle size.
Since the materials are very expensive, I'm looking for an efficient experimental design that can provide meaningful insights while minimizing material usage.
Can you please suggest how I should go about designing this experiment?
thank you
Hello @YanivD ,
Please find below my thoughts:
Generically, in an attempt to better understand the relationship amongst the factors, usually I would go with a sequential design of experiment approach, using at the beginning 2-level fractional factorial designs with "bold" (most apart as possible) level settings and tight control of the experimental noise (using different noise strategies, such as randomization, mapping noise factors, holding them constant or even better using them as factors, etc) in order to surface the vital few effects and then using them to progress with the experimentation.
If you are not close to you objective, to save some runs I would start with foldover designs, Res III, to understand the effects size for each factor and their interactions than once you are satisfied with the variation generated in the response you can foldover the design to increase the resolution and start dealing with the confoundings, screening out the non active factors and interactions, then move on with your inference space towards the optimal point. At the very beginning I would not run replicates, instead I would screen out the factors based on the sparsity of effects, using Daniel plots, to understand about the statistical significance and once you are close to your objective (with much fewer factors to deal with) you can use RSM methods to better model the surface near the optimum point, replicates to improve predictions, etc
Now, about your case, could you please provide more info, are those 10 suppliers supplying the same product and you need to choose one of them? Do you know whether the process factors and its noise would impact the performance of each supplier?
Considering each supplier as a block, I would run a fractional factorial design, including the concentration as a design factor, process factors and already mapped noise as factors following the same strategy mentioned above instead of a one factor experiment for each of the suppliers (blocks) and including replicates, however this is up to you given the cost of each run and the information you already have (at the end this strategy may also needs a lot of runs due to the replication)
Once you run the first fractional factorial design for the first block, you might be able to tell the important effects and they would make sense for the other blocks, you can use this knowledge to refine the experiment for the other blocks.
The block with the factorial designs including the process factors might not sound like the strategy with the minimum number of runs, however I believe it is going to help you build a solid knowledge foundation about your process factors and their interaction with the supplier material.
Please let me know if you would like to further discuss the topic,
Sincerely,
Dear Victor,
Thank you so much for your ideas and solutions - as always including the links are valuable and can help.
all the best,
Yaniv
Hi Emmanuel,
Thank you for the great points and questions - we will try
All the best,
Yaniv
