Efficient planning of peen-forming patterns via artificial neural networks

Autor: Siguerdidjane, Wassime, Khameneifar, Farbod, Gosselin, Frédérick P.
Rok vydání: 2020
Předmět:
Druh dokumentu: Working Paper
DOI: 10.1016/j.mfglet.2020.08.001
Popis: 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.
Databáze: arXiv