Objective
- 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
Contact
Prof. Dr.-Ing., apl. Professor Marcus Stoffel
RWTH Aachen
Eilfschornsteinstraße 18
52062 Aachen
Room 403
Tel.: +49 241 80 94589
Email: stoffel@iam.rwth-aachen.de
Rutwik Gulakala
Email: gulakala@iam.rwth-aachen.de
Aditya Borse
Email: borse@iam.rwth-aachen.de