Machine learning assisted characterisation and simulation of compressive damage in composite laminates

Abstract A data-rich framework is presented to consistently characterise the macroscopic strain-softening response of laminated composites subjected to compressive loading. First, a highly efficient continuum damage finite element (FE) model is used to simulate compact compression tests of quasi-isotropic IM7/8552 carbon fibre-reinforced polymers in order to generate a large virtual dataset for training of machine learning (ML) models. Then, two ML methods, one based on theory-guided neural network architecture to solve the inverse FE problem, and one based on recurrent neural networks with Long Short-Term Memory (LSTM) architecture to solve the forward FE problem, are trained and predictive capabilities are compared. It is found that theory-guided ML for the inverse FE problem yields high loss values and is not applicable to compressive damage characterisation whereas a minimum number of 5,000 FE simulations are needed to train accurate LSTM models for the forward problem. Numerical calibration using the trained LSTM model is validated successfully against experimental data obtained from a wide range of compressive tests including size effect studies in open-hole compression tests and axial crush tests of composite tubes. The proposed strategy demonstrates the effectiveness and challenges of ML to reduce experimental efforts for damage characterisation in composites subjected to compressive loads.