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.
@article{arxiv.2008.08049,
title = {Efficient planning of peen-forming patterns via artificial neural networks},
author = {Wassime Siguerdidjane and Farbod Khameneifar and Frédérick P. Gosselin},
journal= {arXiv preprint arXiv:2008.08049},
year = {2020}
}