Failure Estimation of the Composite Laminates in Layup Optimization Using Finite Element Analysis and Deep Learning

Abstract

Layup optimization of the composite laminates is a very complex problem due to the convoluted multidimensional solution space which is usually explored by addressing different heuristic methods from which the most reliable are the genetic algorithms (GA). The optimization process converges by evaluating a lot of layup configurations which imply that the evaluation should be not only robust but also very fast. The most accurate numerical tool used to simulate the mechanical behavior of the composite laminates is the finite element analysis (FEA) which unfortunately is a computational intensive method. Some studies proposed very fast FEA models specially designed for the layup optimization with the lower bound of the execution time determined by the global linear system solving. Other studies pushed this bound even lower using classical machine learning techniques trained with prior observations (layup configurations) evaluated with FEA. It has been shown that the trained models can successfully replace the computational intensive FEA. The results are very important because the optimization time is dramatically reduced, while the estimation errors induced by the statistical models are acceptable. In this paper, we propose different deep neural network architectures such as multilayer perceptron (MLP) and convolutional and recurrent neural networks (CNN and RNN) that significantly reduce the estimation errors. For example, the classification error reduces from 2% to zero compared to previous studies, for the same numerical example. Also, we use different sets of predictors which allow the failure estimation for each layer in the composite laminate opposite to the previous studies which model the failure response only for the whole structure.