Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (10): 1341-1348.doi: 10.16183/j.cnki.jsjtu.2021.126

• Materials Science and Engineering • Previous Articles     Next Articles

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. 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


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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