A graph neural network surrogate model for mesh-based crashworthiness prediction of vehicle panel components
arXiv:2503.17386v2 Announce Type: replace-cross Abstract: Crashworthiness is a key performance measure in the design of safety-critical vehicle panel components such as B-pillars. Finite element (FE) simulations are widely used to evaluate crash responses but remain computationally expensive for large-scale, nonlinear impact scenarios, particularly when integrated into iterative design and optimisation processes. Although machine learning-based surrogate models have been developed for rapid crashworthiness analysis, they exhibit limitations in detailed representation of complex 3-dimensional c
The increasing computational demands of complex engineering simulations are driving the need for more efficient AI-based surrogate models to accelerate design cycles.
This development allows for faster, more cost-effective crashworthiness analysis, enabling rapid iteration in vehicle design and potentially enhancing safety standards.
Traditional computationally intensive finite element simulations for vehicle crashworthiness can be significantly accelerated and potentially integrated more seamlessly into real-time design processes.
- · Automotive industry
- · AI/ML engineering simulation companies
- · Product designers
- · Consumers (via enhanced safety)
- · Traditional FEA software providers (if they don't adapt)
- · Consulting firms reliant on lengthy simulation projects
Reduced time and cost in vehicle safety design and optimization cycles.
Potential for designing more resilient and adaptable vehicle structures through rapid simulation.
Broad application of AI surrogate models to other computationally intensive engineering fields beyond automotive.
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Read at arXiv cs.LG