Prediction of stiffness and strength in carbon-glass pseudo ductile hybrid composite using computational approach

Abstract

The goal of this article was to integrate finite element analysis (FEA), multi-layer perception (MLP) and transfer learning based convolutional neural network (CNN) for predicting stiffness and strength of woven carbon-glass/epoxy pseudo ductile hybrid composites (PDHCs). To minimize the cost of experimental campaigns, research trends have shifted toward optimizing flight range, speed and battery capacity optimization of electric vertical take-off and landing (eVTOL) structure, as well as utilization of computational frameworks in mechanical property prediction and crashworthiness analysis. However, due to complex failure mechanisms of composites, it was very challenging to predict mechanical properties of PDHCs using conventional FEA. As such, it was very essential to train accurate machine learning model to reduce the hassle of computationally intensive FEA. The CNN model was trained using FEA geometric image dataset, achieving an accuracy of 89.77% and 90.91% sensitivity. The MLP model was trained to map CNN predicted images to detailed numerical outcomes demonstrating excellent convergence with validation and training losses of 0.060 and 0.062, respectively, using a 15-layer architecture with swish activation and regularization. The pseudo-ductile hybrid material developed had showed better performance with large plastic deformation resembling ductile materials. The stress strain response was dominated by carbon plies in linear elastic zone followed by gradual fragmentation, matrix cracking, fiber pull out and debonding with the outer glass plies arresting crack. The numerical comparison of FEM predictions were closely matched with the experimental data in the literature with absolute error less than 5% for tensile strength, tensile modulus, flexural strength and impact strength. The proposed approach gives an insight to overcome the limitations of conventional models for woven hybrids, enabling optimized PDHC design for electric vertical take-off and landing aircraft (eVTOL) applications.