Response surface VS Mixture response surface

[#1]

Hello Gentlemen,

I am writing because I am a little confused about the options that there are in JMP. I am trying to evaluate a DOE and the construction of the model Effect there are several option like response surface which is by default but I did a comparison with Minitab and I found different results between both softwares but I think the Minitab result is the good one because it is more according to the factor plots that I have previously so I started to play a little bit with the option and I found that mixture response surface was the closest one. Why is this happening? I tried to find more information about that in the JMP tutorial and there is lack of information

Note: my DOE is built by 4 factors and I’ve several response

Best regards

FactorialPareto

[#2]

I'm a bit confused on several aspects.

Please explain what design was created. Was it created in JMP? 

Was it a mixture design? A mixture design has a constraint that some or all of the factors add up to a specific amount, usually 1. If this is NOT the case, ignore the mixture response surface option.

Could you possibly share the data? 

I am also guessing since you had several responses, the JMP Fit Model dialog had all fo the responses listed in the Y role of the dialog box. If this is the case, then the Effect Summary table that you show is actually listing the LOWEST p-value for each model term ACROSS ALL RESPONSES. (a common point of confusion).

People on this discussion board will be happy to help, but we do need more details in order to help.

[#3]

It did a full factorial  but I cut one part because one parameter was useless in my experiment. It is more like a customized DOE (My DOE is full factorial 3x2x3x3=54 test but I did 45.) and it was created in JMP. My doubt is in the section of Fit model and more specifically in the part of construct model effect, see picture. The option by default is response surface but apparently the result is a different from Minitab so I started to play and I found that mixture response surface is the closest result one, my question is why is that happing? What are the differences between both?. Do you have an email to send you my data?

Fit Model Dialog

[#4]

You designed a full factorial, but you did not complete a full factorial. That could be leading to your confusing results. Not completing some runs is NOT equivalent to a custom design. Custom designs are optimal for the model that is specified. Missing some of the trials may actually lead to some model terms not being estimable.

The confusion with the model window cannot be answered without understanding the data.

As I said in my first post, a mixture response surface model is to be used only when you have mixture factors. You can ignore it because it does not apply to your situation. If you use it, you will essentially be fitting a no-intercept model which completely changes all of the mathematics and interpretations of your model. Don’t use it.

From your Pareto screen capture, it appears that the last term is not being tested (a missing value for the effect). This leads me to believe that your missing runs are not allowing you to fit the model that you desire. Do you see a Singularity Details part in your JMP report? If so then I am correct. You need to build a model that your data will allow you to estimate.

And finally, the default response surface model in JMP is the default because it is the model associated with your full factorial design. You did not run that full design. Therefore, the model is likely not correct. You changed the design, you need to change your model because those things go together.

[#5]

How can i get my DOE like i want? let me explain better i am working with machinering parameters so i have three tools, two of them are caplable to cut in tu conditions example 1 and 5 but one tool is a special case so it can only cut at 1, so that was the reason i just delate the part of the 5 from the full factorial for only one tool. How can i develop a DOE with this caracteristics?

Best regards

Desired DOE

[#6]

Updated information: related to the previous email, i splited my DOE in two full factorial, one DOE with a width of cut of 1 and the other one with 4.3, i did this in order to fit my response with the DOE model and got the following result for a width of cut 1 but it doesn't work because i am also interested in that response

FirstResult 2Result

[#7]

You would not want a full factorial design because you could not complete it. A custom design with a disallowed combination would be a better choice. You should refer to the design of experiments guide in the Books section under the Help menu.

Also note that you would not be able to estimate the interaction between tool and width of cut because you will not be running all of those combinations. That term should not be in your model.

[#8]

Hello, I read the tutorial and i made a custom design with a disallowed combination and honestly, i don't know why it gave me a strange table with some duplicate values and sometimes with some misses values i tested the combination of too and width of cut and it works well, then i tried tool, width of cut and cuttining speed and was okay too: however, when i did the 4 together, it starts the disaster and found some duplicated values and some missed values. Could you tell me why, I post all my parameters and the table. could you help me please?

Best regards

DOEParameters

[#9]

Updated information: Even for three factors, i have some repeat parameters. Is there a way to block repeat values? Uploaded picture

3 factors

[#10]

Updated information: Even for three factors, i have some repeat parameters. Is there a way to block repeat values? Uploaded picture

3 factors