|About this Abstract
||NUMISHEET 2022: The 12th International Conference on Numerical Simulation of 3D Sheet Metal Forming Processes
||An Advanced Machine Learning Model to Accelerate Sheet Forming Simulations
||Daniel Connolly, Kaan Inal
|On-Site Speaker (Planned)
In modern automotive engineering, vehicles are primarily designed in the virtual space to enable a rapid vehicle design process. This can lead to rapid improvements in vehicle design as more design cycles may be performed. However, this process is heavily constrained by the time and computational requirements necessary to generate the vast number of simulations conducted for vehicle design. Fortunately, modern artificial intelligence (AI) techniques may be used to drastically accelerate the generation of new simulation results. This work presents for the first time an advanced recurrent deep learning framework capable of predicting the entire simulated stamping response for new geometries, materials, and process parameters. The predicted response is compared to simulated finite element models demonstrating significant reduction in prediction time while maintaining high accuracy in key performance indicators of the stamping process. Finally, this work also highlights how this framework may be leveraged to further improve metal forming processes.
||Definite: At-meeting proceedings