Comparative Study of Machine Learning Approaches for Predicting Creep Behavior of Polyurethane Elastomer

Polymers

Wuning Ma¹  Chunhao Yang¹  Jianlin Zhong¹  Zhendong Zhang¹ 

[1] School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;

关键词: creep behavior;    polyurethane elastomer;    time–strain curve;    machine learning;    genetic algorithm;   

DOI  :  10.3390/polym13111768

来源: DOAJ

【 摘 要 】

The long-term mechanical properties of viscoelastic polymers are among their most important aspects. In the present research, a machine learning approach was proposed for creep properties’ prediction of polyurethane elastomer considering the effect of creep time, creep temperature, creep stress and the hardness of the material. The approaches are based on multilayer perceptron network, random forest and support vector machine regression, respectively. While the genetic algorithm and k-fold cross-validation were used to tune the hyper-parameters. The results showed that the three models all proposed excellent fitting ability for the training set. Moreover, the three models had different prediction capabilities for the testing set by focusing on various changing factors. The correlation coefficient values between the predicted and experimental strains were larger than 0.913 (mostly larger than 0.998) on the testing set when choosing the reasonable model.

【 授权许可】

Unknown