Efficient planning of peen-forming patterns via artificial neural networks
This addresses automation challenges in manufacturing by providing an efficient solution for a specific industrial process.
The paper tackled the problem of real-time planning for shot peen forming patterns by using a neural network to learn from simulation data, achieving an average binary accuracy of 98.8% in microseconds.
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.