I have about 400 pieces of silver of different geometric dimensions. They were assigned to six groups and each group went through a series of stress tests, such as bending, pulling, putting in fire for a period of time, etc. The treatments that were given to the six groups were not the same, but fairly similar. The sizes of the six groups were not the same. The pieces either broke at some stage and that was recorded as a success or didn't, which was recorded as a failure. The time of each success was also recorded. The number of successes was about 80.
My goal is build a predictive model to determine if a piece of silver breaks based on its physical dimensions and the treatment it goes through.
I have been somewhat successful in building a model using the physical dimensions, but adding various aspects of the treatment (eg. total time spent in fire) didn't improve the performance at all. I have even tried to build features (eg.total stress on the metal in various directions, total strain on the metal, etc.) based on the physical dimensions and the treatment, for each individual piece, but even these didn't add any predictive performance.
How can I incorporate the treatment information in a way that adds to my predictive power? It is clear that the treatment is a factor in whether a piece breaks or not, and it should somehow show up somewhere.
N.B. I didn't have any control over the design of the treatment, and testing more samples with other treatments is not an option for me.
I'd very much appreciate any suggestions or comments.