Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8050449 | Procedia CIRP | 2018 | 6 Pages |
Abstract
Global optimization methods require a numerous process evaluations to reach the optimum. While tests can be simulated by Finite Element Method (FEM), most of them were substituted by a Neural Network model. Neural Network training is less sensitive to problem dimension than standard Design of Experiments. The approach is assessed against the traditional Finite Element Optimization by exploiting a case study of a steel disc.
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Authors
Doriana M. D'Addona, Dario Antonelli,