Efficient planning of peen-forming patterns via artificial neural networks

• URL: http://arxiv.org/abs/2008.08049v1
• Date: Tue, 18 Aug 2020 17:17:46 GMT
• Title: Efficient planning of peen-forming patterns via artificial neural networks
• Authors: Wassime Siguerdidjane, Farbod Khameneifar, Fr\'ed\'erick P. Gosselin
• Abstract summary: A neural network (NN) learns the nonlinear function that relates a given target shape (input) to its optimal peening pattern (output) The trained NN yields patterns with an average binary accuracy of 98.8% with respect to the ground truth in microseconds.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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