Data-Driven Machine Learning Enhanced Optimisation of Vehicle Crashworthiness Design

Prof. Dr.-Ing., apl. Professor Marcus Stoffel (Aachen)


  • Vehicle design: Interdependent complex systems
  • Importance of crashworthiness: Average 300k road accidents in Germany (Source: UNECE Publication)
  • Strict regulations standards: Euro NCAP, NHTSA for US
  • Increasing complexity- Light weight structure with complex materials
  • Increase in computational cost
  • Need for powerful yet optimized computational strategies

Data Generation

  • RL based approach needs data
  • Data: Plastic deformation, energy absorption, impact load, displacements
  • Data collection methods:
    • Automated Finite Elements (FE) simulations
    • Deep Convolution Generative Adversarial Network (DCGAN): Synthetic data

Predictive Machine Learning Framework

  • Intricated ML network:
    • FEM output data and DCGAN1 > Synthetic data
    • Synthetic data > SLNN > DCGAN2
  • DCGAN2: To predict the overall vehicle response
  • Optimized methods with improved efficiency

Preliminary work:

  • GAN and CNN
  • FE simulation of crash box
  • Graph network-based surrogate

How can we support other projects?

  • AI based modelling
  • Automation
  • GAN, RL
  • Mechanische modelling

How could other projects support our work?

  • Providing other applications / examples


Prof. Dr.-Ing., apl. Professor Marcus Stoffel
RWTH Aachen
Eilfschornsteinstraße 18
52062 Aachen
Room 403
Tel.: +49 241 80 94589

Further Information

Rutwik Gulakala

Aditya Borse

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