Data-driven machine learning enhanced optimisation of vehicle crashworthiness design

数据驱动的机器学习增强了车辆耐撞性设计的优化

• 批准号: 501877598
• 负责人: Professor Dr.-Ing. Marcus Stoffel
• 金额: --
• 依托单位: Lehrstuhl und Institut für Allgemeine Mechanik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/501877598?language=en
• 关键词: Data; driven; machine; learning; enhanced

Design of Crashworthiness is a key aspect of vehicle design. The further evolution of advanced vehicle safety and usage of lightweight structures require powerful optimization strategies. Vehicle crash simulations are computationally intensive and often surrogate models are used in place of the full vehicle model to reduce the complexity of the model and computational effort. Although these models save time and computational cost, the results are sub-optimal and in the case of physical surrogates, the overall vehicle response cannot be determined due to the reduction of the model. In this study, we utilize machine learning (ML) methods to address these issues. Reinforcement learning (RL), which is a subset of ML, is a powerful optimization tool but has rarely been utilized in vehicle design. It has the potential to learn from experience and has the potential to generate near-optimal parameters. In this study, two novel Deep convolutional generative adversarial network (DCGAN) based approaches, an RL approach based on a soft actor-critic agent (SAC), and two supervised learning neural networks (SLNN) are proposed to investigate multidimensional optimization of crashworthiness of a vehicle. The first DCGAN is used to generate synthetic data for training the first SLNN along with simulation data to improve training accuracy. The second SLNN is trained as a mathematical surrogate for continuum material models to accelerate FE simulation (with a patent of the applicant). Due to its sample efficient learning and entropy maximization capability and stability, a SAC agent-based RL framework is used to optimize the vehicle crashworthiness design. The first SLNN is then used as the environment for the proposed deep SAC agent-based RL network which optimizes the design parameters. Finally, the second DCGAN is used to estimate overall vehicle response from reduced surrogate models. The study aims to modernize and optimize the design optimization process in vehicle crashworthiness design reducing overall computational effort and improving accuracy compared to existing surrogate models.

耐撞性设计是汽车设计的一个重要方面。先进车辆安全性的进一步发展和轻量化结构的使用需要强有力的优化策略。车辆碰撞仿真计算量大，通常使用替代模型代替整车模型，以降低模型的复杂性和计算量。虽然这些模型节省了时间和计算成本，但结果不是最优的，并且在物理替代的情况下，由于模型的减少，无法确定车辆的整体响应。在本研究中，我们利用机器学习（ML）方法来解决这些问题。强化学习（RL）是机器学习的一个子集，是一种强大的优化工具，但很少用于车辆设计。它有可能从经验中学习，并有可能产生接近最优的参数。在这项研究中，提出了两种新颖的基于深度卷积生成对抗网络（DCGAN）的方法，一种基于软行为-评论代理（SAC）的强化学习方法，以及两种监督学习神经网络（SLNN）来研究车辆耐撞性的多维优化。第一个DCGAN与仿真数据一起生成用于训练第一个SLNN的合成数据，以提高训练精度。第二个SLNN被训练为连续介质材料模型的数学代理，以加速有限元模拟（申请人的专利）。基于SAC智能体的强化学习框架具有样本高效学习和熵最大化的能力和稳定性，可用于优化车辆的耐撞性设计。然后将第一个SLNN用作所提出的基于深度SAC代理的RL网络的环境，该网络优化了设计参数。最后，第二个DCGAN用于从简化的代理模型中估计整体车辆响应。与现有替代模型相比，该研究旨在实现汽车耐撞设计优化过程的现代化和优化，减少总体计算量，提高精度。