Machine learning prediction in distortion behavior of unsymmetric laminates under hygrothermal environment

This paper deals with the effects of curing process and hygrothermal environment on the distortion behaviors of continuous carbon fiber reinforced polyamide 6 (CF/PA6) unsymmetric laminates. To accurately characterize the shape of CF/PA6 unsymmetric laminates in the water absorption process, the 3D model reconstruction with different water content is carried by combining 3D scanner and secondary development in Abaqus. A full-field displacement comparison method is proposed to calculate the equivalent thermal/moisture expansion coefficient, and the effectiveness of numerical simulation is verified. The dataset with 2816 instances is further constructed through finite element method. Through grid search and five-fold cross validation, the ANN model is trained and validated according to R 2 and MSE criterion. The well-trained ANN model builds the mapping relationship between lay-up design parameters, hygrothermal environment and the distortion parameters of unsymmetric laminates.