Elastic-plastic modeling method based on temporal convolutional network

Haibo Wang; Qiang Niu

doi:10.1117/12.3004602

16 August 2023 Elastic-plastic modeling method based on temporal convolutional network

Haibo Wang, Qiang Niu

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 1278725 (2023) https://doi.org/10.1117/12.3004602
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

Because the deformation of the material under load is strongly correlated with the loading path, the Temporal Convolutional Network (TCN), which can satisfy the time-series learning, is chosen to model the elastic-plastic intrinsic relationship of the material. The ABAQUS software is used to write scripts to construct the data set required for training. After training and learning the stress-strain states and their increments for different loading states, the constructed network model can well characterize the yielding process and the subsequent yielding evolution of the material. Compared with the traditional phenomenological model, it has better flexibility and computational efficiency, and provides a new method for constructing the constitutive model of materials.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haibo Wang and Qiang Niu "Elastic-plastic modeling method based on temporal convolutional network", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 1278725 (16 August 2023); https://doi.org/10.1117/12.3004602

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