人工神经网络
空隙(复合材料)
实验数据
有限元法
结构工程
本构方程
可塑性
材料科学
计算机科学
人工智能
工程类
数学
复合材料
统计
作者
Alexander Schowtjak,J. Gerlach,Waqas Muhammad,Abhijit Brahme,Till Clausmeyer,Kaan Inal,A. Erman Tekkaya
标识
DOI:10.1016/j.ijsolstr.2022.111950
摘要
A model for the evolution of ductile damage in the sense of void fractions using artificial neural networks (ANN) is proposed. In contrast to constitutive damage models, the damage prediction is solely based on experimental data and has no underlying assumptions for the damage evolution law. This guarantees that the experimental observations are captured correctly. High resolution experimental void data obtained by scanning electron microscopy with a minimum single void area of 0.02 μm2 are used as training data. The loading state is obtained from finite element simulations. The equivalent plastic strain is used to describe the load amplitude and the triaxiality as well as the normalised Lode angle are utilised to characterise the loading type. Different strategies for the training of the model as well as the prediction are analysed. The model is used to visualise the loading state-dependent damage evolution. Furthermore, it is applied to two different bending processes. It is shown that the prediction quality highly depends on the experimental and numerical data used for training. If the loading states of the application problem are within the domain of the training data, the prediction quality is good and even better than constitutive models used in literature.
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