Hi everybody and thanks for reading!
My challange is to model wear of objects, like drills. I'm not sure if it is solvable with a neuronal network.
I have time resolved input data which the wear is correlating with. And I have targets which are the measurements of the wear after some time. So I do not have a target for each time-step, but lots of rows of inputs.
I have n=20 input tables, always 3 columns and variating amount of rows. For each input table I have one target value. Is there a way to create a predictive model for that using NN? The current solution is to create an alytical model with which I calculate a wear for each row, sumarize the wear for each input table and then optimize with the global optimization. However, I'm not happy with the results.
Thanks and best wishes Michael