  Robust automation of the shot peen forming process demands a closed-loop
feedback in which a suitable treatment pattern needs to be found in real-time
for each treatment iteration. In this work, we present a method for finding the
peen-forming patterns, based on a neural network (NN), which learns the
nonlinear function that relates a given target shape (input) to its optimal
peening pattern (output), from data generated by finite element simulations.
The trained NN yields patterns with an average binary accuracy of 98.8\% with
respect to the ground truth in microseconds.
