Determination of constitutive properties from spherical indentation data using neural networks. Part I: the case of pure kinematic hardening in plasticity laws
Determination of constitutive properties from spherical indentation data using neural networks. Part I: the case of pure kinematic hardening in plasticity laws
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DOI：
10.1016/s0022-5096(98)00109-4
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发表时间：
1999-07-01
影响因子：
5.3
通讯作者：
Tsakmakis, C
中科院分区：
文献类型：
--
作者：
Huber, N;Tsakmakis, C
在本文中，针对一个学术问题（给定杨氏模量的纯运动硬化），展示了神经网络从球形压痕获得的数据中识别材料参数的能力。为了获得用于神经网络训练和验证的数据基础，针对各种材料参数集进行了大量的有限元模拟。描述有限变形塑性的本构模型是用阿姆斯特朗 - 弗雷德里克型非线性运动硬化来构建的。胡贝尔和察马克基斯（1998a）表明，对于给定的材料参数，由压头的加载、卸载和再加载组成的循环压痕过程的深度 - 载荷响应呈现出一个典型的滞后环。使用神经网络解决了将深度 - 载荷响应反推回本构方程中相关参数的逆问题。©1999爱思唯尔科学有限公司。保留所有权利。
In this paper the power of neural networks in identifying material parameters from data obtained by spherical indentation is demonstrated for an academic problem (pure kinematic hardening, given Young's modulus). To obtain a data basis for the training and validation of the neural network, numerous finite element simulations were carried out for various sets of material parameters. The constitutive model describing finite deformation plasticity is formulated with nonlinear kinematic hardening of Armstrong-Frederick type. It was shown by Huber and Tsakmakis (1998a) that the depth-load response of a cyclic indentation process, consisting of loading, unloading and reloading of the indenter displays a typical hysteresis loop for given material parameters. The inverse problem of leading the depth-load response back to the related parameters in the constitutive equations is solved using a neutral network. (C) 1999 Elsevier Science Ltd. All rights reserved.