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.
The low-velocity impact response of the sandwich curved panels with functionally graded carbonnanotube-reinforced composite (FG-CNTRC) surface and isotropic foam core is discussed in this paper. Five types of stacking arrangements including uniform distribution of FG-CNTRC considering thermal environment were analysed. Using the Hertz contact law and rule of mixture model as well as the Kármán-type equations, the nonlinear formulations were built and solved by the two-step perturbation method. The carbon nanotubes' volume fraction, the structure size, the original impact velocity, temperature, relative thickness and the influence of gradient forms on the panels' impact behaviours were analysed. The outcomes show that the stiffness of the non-contact surface has large influence on contact response and various types of FG-CNTRC can be used for different operating conditions providing stiffness or cushion performance.
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