基于三维卷积神经网络的微结构性能快速预测

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关键词：神经网络；微结构；数值均质化；水平集方法

中图分类号：TB383

DOI：10.3969/j.issn.1004-132X.2025.11.026

Abstract：The significant scale difference between microstructure and macro structure，and the coupling of complex micro-geometric configuration and substrate properties which make the analysis of macroequivalent performance of microstructure is very dificult. Therefore，a prediction model of microstructure homogenization elastic tensor was proposed based on three-dimensional convolutional neural network.A parametric modeling of microstructure was completed by level set method，and the equivalent elastic tensor of microstructure was calculated by numerical homogenization. A data representation method coupling geometric configuration and substrate properties was proposed to match the mixed inputs and equivalent elastic tensor labels，and the matched data samples were used as the dataset for neural network training. Finally， model performance was analyzed from partial errors of the predicted results and the calculation efficiency. The proposed model may significantly improve the performance analysis eficiency of the microstructure within the allowable error range.

Key words： neural network；microstructure;numerical homogenization;level-set method

0 引言

微结构广泛存在于自然界中，并具有优良的物理性能，因而受到国内外学者的高度关注。（剩余14169字）