Gurson-Tvergaard-Needleman parameters identification using artificial neural networks in sheet metal blanking
作者： H. Aguir , H. Marouani
DOI: 10.1007/S12289-010-0720-5
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摘要: This paper presents a method for the identification of large used coupled damage model parameters in sheet metal blanking. The existing finite element models describe easily elastoplastic behaviour occurring during However, description evolution is much more delicate to appreciate. proposed connects (FEM) with artificial neural networks (ANN) analysis order identify values Gurson-Tvergaard-Needleman (GTN) parameters. Blanking tests are carried out obtain experimental material response under loading. A compute load displacement curve depending on GTN Via full design experiments, numerical database built up, which ANN training. properties done by minimizing error between an and predicted one function. identified law validated other configurations blanking tests.
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