上海交通大学学报

上海交通大学学报 >

2022 , Vol. 56 >Issue 10: 1341 - 1348

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.126

材料科学与工程

反向传播神经网络联合遗传算法对复合材料模量的预测

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  • 1.上海交通大学 航空航天学院,上海 200240
    2.中国商用飞机有限责任公司 复合材料中心,上海 201210
    3.北京航空航天大学 材料科学与工程学院, 北京 100191
王卓鑫(1995-),女,山西省晋中市人,硕士生,从事复合材料性能的机器学习研究.

收稿日期: 2021-04-09

  网络出版日期: 2022-06-28

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Prediction of Modulus of Composite Materials by BP Neural Network Optimized by Genetic Algorithm

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  • 1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China
    2. Composites Centre, Commercial Aircraft Corporation of China, Ltd., Shanghai 201210, China
    3. School of Materials Science and Engineering, Beihang University, Beijing 100191, China

Received date: 2021-04-09

  Online published: 2022-06-28

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摘要

为减少测试成本和缩短设计周期,基于机器学习方法对树脂基复合材料模量的预报方法进行了研究.采用一种全新预测方法——神经网络联合遗传算法(GA-ANN),将T800/环氧复合材料的强度、泊松比和失效应变作为反向传播(BP)神经网络的3个输入变量,在遗传算法(GA)中得出最优阈值和权重,并将所得数值赋给对应的网络参数,更新BP神经网络以更高的准确率预测树脂基复合材料的模量;同等条件下,用Adam算法进行预测.对比这两种方法,结果充分证明了GA-ANN的可行性.

关键词： 机器学习; 反向传播神经网络; 遗传算法; 复合材料模量; Adam算法

本文引用格式

王卓鑫, 赵海涛, 谢月涵, 任翰韬, 袁明清, 张博明, 陈吉安 . 反向传播神经网络联合遗传算法对复合材料模量的预测[J]. 上海交通大学学报, 2022 , 56(10) : 1341 -1348 . DOI: 10.16183/j.cnki.jsjtu.2021.126

Abstract

In order to reduce the cost of testing and shorten the design cycle, this paper studies the prediction method of the modulus of resin matrix composites based on the machine learning method. Using a new prediction method — the neural network in combination with the genetic algorithm (GA-ANN), the strength, the Poisson’s ratio, and the failure strain of the T800/epoxy composite material are used as three input variables of the back propagation (BP) neural network. Then, the optimal threshold and weight are obtained in the genetic algorithm (GA), which are assigned to the corresponding network parameters, and the BP neural network is updated for higher accuracy to predict the modulus of resin matrix composites. Under the same conditions, the Adam algorithm is used to predict. A comparison of these two methods fully proves the feasibility of the GA-ANN algorithm.

Key words： machine learning; back propagation (BP) neural network; genetic algorithm; composite material modulus; Adam algorithm

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