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2024年3月26日发(作者：二进制转换成十进制的公式)
卷积神经网络机器学习相关外文翻译中英文
英文
2020
Prediction of composite microstructure stress-strain curves using
convolutional neural networks
Charles Yang，Youngsoo Kim，Seunghwa Ryu，Grace Gu
Abstract
Stress-strain curves are an important representation of a material's
mechanical properties, from which important properties such as elastic
modulus, strength, and toughness, are defined. However, generating
stress-strain curves from numerical methods such as finite element
method (FEM) is computationally intensive, especially when considering
the entire failure path for a material. As a result, it is difficult to perform
high throughput computational design of materials with large design
spaces, especially when considering mechanical responses beyond the
elastic limit. In this work, a combination of principal component analysis
(PCA) and convolutional neural networks (CNN) are used to predict the
entire stress-strain behavior of binary composites evaluated over the
entire failure path, motivated by the significantly faster inference speed of
empirical models. We show that PCA transforms the stress-strain curves
into an effective latent space by visualizing the eigenbasis of PCA.
Despite having a dataset of only 10-27% of possible microstructure
configurations, the mean absolute error of the prediction is <10% of the
1
range of values in the dataset, when measuring model performance based
on derived material descriptors, such as modulus, strength, and toughness.
Our study demonstrates the potential to use machine learning to
accelerate material design, characterization, and optimization.
Keywords：Machine learning，Convolutional neural networks，
Mechanical properties，Microstructure，Computational mechanics
Introduction
Understanding the relationship between structure and property for
materials is a seminal problem in material science, with significant
applications for designing next-generation materials. A primary
motivating example is designing composite microstructures for
load-bearing applications, as composites offer advantageously high
specific strength and specific toughness. Recent advancements in additive
manufacturing have facilitated the fabrication of complex composite
structures, and as a result, a variety of complex designs have been
fabricated and tested via 3D-printing methods. While more advanced
manufacturing techniques are opening up unprecedented opportunities for
advanced materials and novel functionalities, identifying microstructures
with desirable properties is a difficult optimization problem.
One method of identifying optimal composite designs is by
constructing analytical theories. For conventional
particulate/fiber-reinforced composites, a variety of homogenization
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