反向传播神经网络联合遗传算法对复合材料模量的预测

doi:10.16183/j.cnki.jsjtu.2021.126

上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (10): 1341-1348.doi: 10.16183/j.cnki.jsjtu.2021.126

所属专题：《上海交通大学学报》2022年“材料科学与工程”专题

反向传播神经网络联合遗传算法对复合材料模量的预测

王卓鑫¹, 赵海涛¹(), 谢月涵², 任翰韬², 袁明清¹, 张博明³, 陈吉安¹

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

收稿日期:2021-04-09 出版日期:2022-10-28 发布日期:2022-11-03
通讯作者: 赵海涛 E-mail:zht@sjtu.edu.cn.
作者简介: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.

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

WANG Zhuoxin¹, ZHAO Haitao¹(), XIE Yuehan², REN Hantao², YUAN Mingqing¹, ZHANG Boming³, CHEN Ji’an¹

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:2021-04-09 Online:2022-10-28 Published:2022-11-03
Contact: ZHAO Haitao E-mail:zht@sjtu.edu.cn.

摘要/Abstract

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

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

中图分类号:

TP181
V 258

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.

WANG Zhuoxin, ZHAO Haitao, XIE Yuehan, REN Hantao, YUAN Mingqing, ZHANG Boming, CHEN Ji’an. Prediction of Modulus of Composite Materials by BP Neural Network Optimized by Genetic Algorithm[J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1341-1348.

图/表 11

表1

图1

表2

表3

图2

图3

表4

GA-ANN和Adam-ANN训练集预测表

模型	$e -$ /%		RMS/GPa		SEP/GPa
模型	z₁	z₂	z₁	z₂	z₁	z₂
GA-ANN	1.66	2.47	3.939 9	4.614 8	3.939 9	4.614 3
Adam-ANN	1.64	2.50	3.847 3	4.674 7	3.847 2	4.673 7

表4

表5

GA-ANN和Adam-ANN测试集预测表

模型	$e -$ /%		RMS/GPa		SEP/GPa
模型	z₁	z₂	z₁	z₂	z₁	z₂
GA-ANN	1.83	3.22	4.084 9	7.053 7	4.079 8	5.764 3
Adam-ANN	1.94	3.71	4.254 0	7.960 5	4.203 9	5.923 5

表5

图4

图5

图6

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试验编号	x	ν	y	z₁	z₂
1	0.463 068	0.637 681	0.403 461	0.652 174	0.60
2	0.414 773	0.543 478	0.234 987	0.608 696	0.60
3	0.696 023	0.471 014	0.388 657	0.608 696	0.20
4	0.510 417	0.710 145	0.434 320	0.608 696	0.55
5	0.754 735	0.731 884	0.645 538	0.521 739	0.70
6	0.775 568	0.789 855	0.604 671	0.565 217	0.70
7	0.778 409	0.347 826	0.576 939	0.608 696	0.70

隐藏层结构	e₁	e₂
2	2.087 7	2.765 7
6	2.094 0	2.739 1
10	2.076 5	2.533 3
14	2.068 1	2.647 5

隐藏层结构	e₁	e₂
6, 6	2.062 4	2.568 7
10, 6	2.071 9	2.759 9
12, 10	2.070 3	2.667 9
14, 12	2.027 8	2.580 9

反向传播神经网络联合遗传算法对复合材料模量的预测

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

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