Thank you for providing the files. This helps tremendously.
First, you state that you MUST replicate the Excel file. If that is the case, you are not really interested in creating a designed experiment. You are interested in running the specific trials in the Excel sheet. Why not just open the Excel table into JMP, run those trials, enter the responses and analyze?
You will ALWAYS have difficulty getting JMP to match your Excel table because JMP can do better than that table of runs.
A few issues that I saw from your table:
When specifying your factors, if the factor is continuous (like Feed per Tooth, Cutting Speed, and Width of Cut), then specify that factor as continuous rather than categorical. The only reason to specify them as discrete is to FORCE certain levels into the design. If you truly want to do that, specify a higher-order model and/or use the Discrete Numeric option.
When I made the factors continous (except for Tool Name), I created a 45 run design for the main-effects only model (attached design). I then compared the JMP generated custom design to the Excel table. The JMP custom design had equivalent or better power on all of the model terms. The JMP design had lower prediction variance than the Excel table for a large majority of the design space (and where it was worse, it was extremely close). I have attached the result as a picture. Bottom line is that JMP will not give you an inferior design if a better design exists.
Creating the design in this fashion with JMP does limit the factors to two levels. Experimentation is about EFFICIENCY. Do as few trials as possible to estimate your desired model. Then you use the model to determine the optimal settings. You do not need to try every combination in a grid to optimize a process. With this approach, because you were only specifying a main effects model, there is no need for more than two levels for any of the continuous factors. Conducting experiments at more than two level is wasteful.
Why insist on a third level? Many times it is because you expect a curved response. So, the model to use to estimate curvature is a response surface model. In JMP, I clicked the RSM button to get a response surface model (design attached as JMP RSM Design). That will force a 3rd level because it is needed to estimate a quadratic effect. I then compared this 45 run custom RSM design to the Excel table. Again, the JMP custom RSM design is superior as shown in the attached picture.
Remember that JMP is designed to create the best designed experiment possible, not to match a specific table of runs. The table of runs may not be the best choice. The only way to have JMP "create" a table to match the Excel table is to use DOE > Classical > Full Factorial. Specify all of the factors and create the design. Then you must manually remove the unwanted combination. Note that the last piece must be manual because if you do not do that combination you are NOT running a full factorial design. That is why JMP does not build that design automatically. That is the purpose of the custom design: create designs for your specific situation rather than trying to force fit a design that is not really appropriate.
You might wish to take a class on design of experiments or perhaps look at the Optimal Design of Experiments book by Goos and Jones to gain more insights on design creation. I hope this has been helpful.
Compare JMP Design to Excel TableCompare JMP RSM Design to Excel Table
Dan Obermiller