A machine learning framework for accelerating the design process using CAE simulations: An application to finite element analysis in structural crashworthiness

Abstract

This paper presents a novel framework for predicting computer-aided engineering (CAE) simulation results using machine learning (ML). The framework is applied to finite element (FE) simulations of dynamic axial crushing of rectangular crush tubes that are typically used in vehicle crashworthiness applications. A virtual design of experiments that varies the size and wall thickness of the FE model is performed to generate the necessary training data. This process generates designs with varying numbers of nodes and elements that are handled by the ML system. However, the explicit design parameters and meshing techniques that were used to generate the training data remain unknown to the ML system. Instead, 3D convolutional neural networks (CNN) autoencoders are used to process the initial FE model data (i.e., nodes, elements, thickness, etc.) to automatically determine these features in an unsupervised manner. A voxelization strategy that operates on the mass of individual nodes is proposed to handle the unstructured nature of the nodes and elements while capturing variations in the wall thickness of the FE models. The flattened latent space generated by the 3D-CNN-autoencoder is then used as input into long-short term memory neural networks (LSTM-NN) to predict the force–displacement response as well as the deformation of the mesh. The training process of both the 3D-CNN-autoencoders and LSTM-NN is systematically studied to highlight the robustness of the framework. The proposed ML system utilizes only 16% of the simulations generated in the virtual design of experiments to achieve good predictive capability. Once trained, the proposed framework can predict the deformation of the mesh and resulting force–displacement response of a new design up to ∼330 and ∼2,960,000 times faster, respectively, than the conventional FE approach with good accuracy. This computational speed up offers design engineers and scientists a potential tool for accelerating the design exploration process with CAE simulation tools, such as FE analysis.