期刊
ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS
卷 28, 期 3, 页码 195-204出版社
ELSEVIER SCI LTD
DOI: 10.1016/S0955-7997(03)00050-X
关键词
cyclic plastic analysis; inverse problem; material modeling; neural network material model; finite element analysis
The computer simulation is replacing mechanical experiments in many cases due to its cost-effectiveness and improved accuracy. Nevertheless, its application fields are still limited to elastic analysis, as there exists a significant amount of model error in present inelastic material models. In this paper, we first propose a material model using neural networks, which has the ability to describe plasticity and cyclic plasticity. The proposed model was first created with the material data of 2 1/4Cr-1 Mo steel, and the results show that the model can represent the actual cyclic plastic material behavior within a 2% error. The model is then implemented in a commercially available finite element analysis package and the cyclic plastic deformation behavior of a test specimen of the material is predicted within a 3% error. (C) 2003 Elsevier Ltd. All rights reserved.
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