Efficient planning of peen-forming patterns via artificial neural networks
| Autor: | Farbod Khameneifar, Wassime Siguerdidjane, Frédérick P. Gosselin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 0209 industrial biotechnology Ground truth Artificial neural network Computer science business.industry FOS: Physical sciences Binary number Forming processes Peening 02 engineering and technology Computational Physics (physics.comp-ph) 021001 nanoscience & nanotechnology Automation Industrial and Manufacturing Engineering Finite element method Machine Learning (cs.LG) Nonlinear system 020901 industrial engineering & automation Mechanics of Materials 0210 nano-technology business Physics - Computational Physics Algorithm |
| Zdroj: | Manufacturing Letters. 25:70-74 |
| ISSN: | 2213-8463 |
| 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: | OpenAIRE |
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