@michael_jmp or @markbailey,   Could one of you address my questions?   Thanks. ... View more

Hi @markbailey,   It took me a while to digest all your inputs. Let me try to summarize my understanding with questions below:   About Screen methods: - Design oriented - "These designs assume main effects only." So they are not designed to study interactions or quadratic terms? - "P-B designs handle interaction effects (i.e., better chance of estimation) than the regular fractional factorial designs." This means P-B designs can handle experiments with main factor interactions, but it does NOT mean we can include the interaction terms in the model, right? - DSD: "unchanged DSD will allow you to estimate the main effects for up to about half of the factors in your design (effect sparsity) and some interaction and quadratic effects (assuming effect heredity)." Say I have seven factors, DSD can estimate the effects of three to four factors, but not all the seven? DSD can estimate some interaction and quadratic effects, but does NOT include interaction and quadratic terms in the model, right?   Taguchi Method - It can be fractional factorial or P-B based - Assumptions include addictiveness (orthogonal) between factors, thus interaction will cause problem - Not sequential, thus not for screening purpose - "It simultaneously screens by spanning the design space with two-level combinations and optimizes by 'pick the winner' based on a S/N ratio derived from the mean and standard deviation of the observed response (not modeling)." - Question: Taguchi Method actually encourages the usage of three level factors so as to catch the nonlinear effect. Why you said the factors area mostly two-levels?   For my condition and goal: 1. I have 7 continuous 3-level factors 2. There will be interactions between factors, but don't know exactly which ones 3. The goal: optimize the system performance (by studying factor effects)   Model-oriented: If I know what (main, interaction and quadratic terms) I want to study, custom design is effective. In addition, if additive condition is met, Taguchi Method can be efficient.    Design-oriented: If I don't know much about effect of main, interaction and quadratic terms, sequential approach with screening design is effective.    Please comment/answer on my understandings/questions.   Thanks, John     ... View more

Hi @markbailey,   Sorry for the confusions. Let me try to clarify them:   SSD "I don't know what you mean by "SSD" but I assume that it is a fractional factorial or Plackett-Burnam design." A: The term of SSD stands for standard screening design which is used by JMP 13 user guide. It is under DOE\Classical \Screening Design.   Option 1: "So you are using an orthogonal array? The L27 design is a P-B design, not an OA." A: I though the L27 here is a Taguchi L27 OA. I was wrong based on your description.   I do believe there are interactions here or there. So I should not use Taguchi L27 OA at this phase per your advice.   "What do you mean by "add 11 pair of 2nd order interactions?" You can't add terms during the design. If you mean that you added these terms later in the Fit Model dialog after clicking Make Table, how did you select these 11 terms out of the potential 21 two-factor interaction effects? Why add these terms to the initial analysis?" A: Yes, I "added these terms later in the Fit Model dialog after clicking Make Table" and by clicking the Interaction command button. I didn't have the options of selecting which interaction to use. I want to study the effect of interactions, but I could not find a way to add them before clicking Make Table.   Option 2: At DOE\Classical \Screening Design, after select factor level and type, JMP gives me two options: "Choose from a list of fractional factorial designs" or "Construct a main effects screening design". I used the latter option, and specified I want to conduct 27 runs of test. After clicking Make Table, I added interaction.   DSD "What do you mean you can add 9 interaction terms? Yes, you will have sufficient runs but this model will be saturated. How do you know which 9 out of 21 to select?" A: Again, I click interaction after clicking Make Table, and I couldn't select which interaction to use. Yes, I just found I could change the number of runs, and there is a upper limit.   "Yes, you can change the factor levels. Such changes may significantly compromise the performance of the resulting design. The best way is to select the factor columns and then select Cols > Recode." A: DSD takes 3-level continuous variables. I can specify low and high values for each factor before Make Design. After that, JMP fit the average value as the mid automatically. If the mid value of my physical sample is different, I will have to manually change it after I click Make Table. Is this going to be problem?   Yes, Custom Design is very versatile. I can add 2nd order interactions and quadratic terms. However, it seems to me that adding terms besides main factors is not supported by JMP screening design (either SSD or DSD). Please clarify and explain why.   Thanks, John ... View more

Hi @markbailey   Thanks a lot for your answer. I want to optimize a mechanical system with say 7 control factors, and each factor can have three levels. In this early stage of the DOE, I want to achieve primarily screening, 2ndly characterization, and 3rdly some degree of optimization. I can afford to run about 30 tests in this phase.   I have test several designs as follows:   SSD (under Classical\Screen) using 3-level discreet numerical variables: - Option 1: A L27 OA is given by JMP. Correlation between main factors and 2nd and 3rd order interaction were analyzed by JMP Color Map. I also was able to add 11 pair of 2nd order interactions. - Option 2: I manually select 27 run of experiments test main factors and add 2nd order interactions (JMP allowed 19 pairs)   DSD using continuous variable - JMP generate a 21-run design, and allowed me to add 9 pair of 2nd order of interactions. - Based on you message, I assume I can replace each factor level with a test sample value. Right? What is efficient way to do this instead of manually change factor level one by one? - Can the test run number be changed from 21 to 27 so as to compare with SSD?   Thanks, John ... View more