Data-driven machine learning enhanced optimisation of vehicle crashworthiness design

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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.