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

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.

Automated summary

The study used a machine learning approach to predict the creep properties of polyurethane elastomers, with three models showing excellent fitting ability for the training set. The models demonstrated different prediction capabilities for the testing set under varying factors, with correlation coefficient values exceeding 0.913 when choosing the appropriate model.