The problem is that you set the modeling type of Temperature to Ordinal. I would use Continuous. But Montgomery used Nominal for both Material and Temperature. When I change the modeling type to match, I get the same result:
I just verified that the data in your data table match the data presented in Doug's book.
I added the usual column properties for factors and responses, but it did not change the outcome. I thought that the significance might be affected if the factor levels were not coded first.
JMP makes sure that all the data types, modeling types, and column properties are properly set when you design the experiment with JMP. You have to manually add or adapt all the meta-data when you enter data by hand.
Doug was illustrating how experimenters and statisticians thought about the analysis 100 years ago when factorial designs were introduced. It is still valid, but it is not the only way to think about it. It is a balanced design and treats all factors as if they were nominal categories. Again, that is still valid but only one way. Why did they do it that way? It was the only way available to them at the time. So that they could perform the analysis by hand. A 'computer' in the 1920s was a job title - no kidding. Modeling the data this way allowed people to compute the simple sums that Doug shows by hand.
Today we use computers and software to perform regression analysis. We can realistically treat Temperature as a continuous effect. We can specify the linear model in very flexible terms and then compute an optimal design for estimating the parameters or the response. The design is fixed in this case but I can propose other modeling types and terms. Here is one possibility:
It might require additional runs to verify one of these models as accurate.