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 $\sim$330 and $\sim$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.

本文提出了一种使用机器学习(ML)预测计算机辅助工程 (CAE) 仿真结果的新框架。该框架应用于矩形挤压管动态轴向挤压的有限元 (FE) 模拟，矩形挤压管通常用于车辆耐撞性应用。执行改变 FE 模型尺寸和壁厚的虚拟实验设计，以生成必要的训练数据。此过程生成的设计具有由ML 系统处理的不同数量的节点和元素。然而，用于生成训练数据的显式设计参数和网格划分技术对于 ML 系统仍然未知。相反，3D 卷积神经网络 (CNN)自动编码器用于处理初始有限元模型数据（即节点、单元、厚度等），以无监督的方式自动确定这些特征. 提出了一种对单个节点的质量进行操作的体素化策略，以处理节点和元素的非结构化性质，同时捕获 FE 模型壁厚的变化。由 3D-CNN 自动编码器生成的扁平潜在空间然后用作输入到长短期记忆神经网络 (LSTM-NN) 以预测力-位移响应以及网格的变形。系统地研究了 3D-CNN 自动编码器和 LSTM-NN 的训练过程，以突出框架的鲁棒性。所提出的 ML 系统仅利用在虚拟实验设计中生成的模拟的 16% 来实现良好的预测能力。一旦经过训练，所提出的框架可以预测网格的变形以及新设计产生的力 - 位移响应高达$～$330 和 $～$分别比传统 FE 方法快 2,960,000 倍，具有良好的精度。这种计算速度为设计工程师和科学家提供了一种潜在工具，可以使用 CAE 仿真工具（例如 FE 分析）加速设计探索过程。