Rapid prediction and inverse design of distortion behaviors of composite materials using artificial neural networks

If the process-induced distortions (PIDs) of asymmetrical laminates can be predicted accurately and tailored at the early design stage, the production of curved panels from flat molds could be an attractive technique in a cost-driven production environment. A data-driven computational methodology which integrates the finite element method (FEM) and artificial neural network (ANN) is presented to rapidly predict the maximum PID and to perform high-throughput screening of thermosetting-matrix composites of an asymmetrical laminate for a targeted maximum PID. We performed a grid search on ANN architectures and hyper-parameters using cross-validation and obtained a well-trained ANN model with high generalization performance. For the forward problem, the ANN model was adopted to predict the maximum PIDs of CYCOM X850 and CYCOM 977-2 prepregs, which were subsequently verified experimentally. For inverse design, a large-scale screening method based on the ANN model was utilized to determine the candidates for a targeted maximum PID, with an experimental demonstration using one of these candidates. The well-trained ANN model provides an alternative approach to faster computation with high accuracy for the maximum PID prediction and further guides the discovery of materials with desired distortion behaviors.

Automated summary

A data-driven computational methodology integrating FEM and ANN is proposed to rapidly predict maximum PID of asymmetrical laminates and perform high-throughput screening of thermosetting-matrix composites for targeted maximum PID, providing a new approach for material design.