CPINet: Parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM
CPINet: Parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM
复制标题
CPINet：基于CNN-LSTM自动去噪的路径依赖本构模型参数识别
DOI：
10.1016/j.euromechsol.2021.104327
复制
发表时间：
2021-06-09
影响因子：
4.1
通讯作者：
Yan, Cheng
中科院分区：
文献类型：
--
作者：
Guo, Zhenfei;Bai, Ruixiang;Yan, Cheng
基于卷积神经网络（CNN）和改进的长短期记忆（LSTM）神经网络，提出了一种深度学习模型CPINet，用于快速准确地识别路径相关本构模型参数，并具有良好的去噪性能。以各向同性强化的弹塑性本构模型为例进行了说明。结果表明，CPINet能够捕捉应变场序列与非时间特征（加载顺序和几何尺寸）之间的复杂关系，实现对材料本构参数的实时、准确识别。去噪分析表明，CNN的去噪处理和应变特征提取为CPINet提供了出色的去噪能力。最后，将识别出的6061铝合金本构参数与CPINet和有限元模型修正方法进行比较，验证了CPINet的有效性。据我们所知，这是第一项证明使用深度学习技术即时准确识别本构参数的可行性和巨大潜力的研究。
Based on convolutional neural network (CNN) and improved long short-term memory (LSTM) neural network, a deep learning model CPINet is proposed for instant and accurate identification of path-dependent constitutive model parameters with excellent denoising performance. The elastic-plastic constitutive model with isotropic hardening is taken as an example for illustration. The results show that the CPINet can capture the intricate relationship between the strain field sequence and the non-temporal features (loading sequence and geometry dimensions) to identify constitutive parameters instantly and accurately. The denoising analysis revealed that the denoising processing and strain feature extraction of CNN provides excellent denoising ability to CPINet. Finally, the CPINet is validated by comparing the identified constitutive parameters of 6061 aluminum alloy with CPINet and finite element model updating method. To our knowledge, this is the first study that demonstrates the feasibility and considerable potential of using a deep learning technique to instantly and accurately identify constitutive parameters.