Efficient planning of peen-forming patterns via artificial neural networks
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2020
- DOI:
- 10.48550/arXiv.2008.08049
- arXiv:
- arXiv:2008.08049
- Bibcode:
- 2020arXiv200808049S
- Keywords:
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- Physics - Computational Physics;
- Computer Science - Machine Learning
- E-Print:
- doi:10.1016/j.mfglet.2020.08.001