Lightweight Metal Stampings Enabled by Artificial Intelligence and Real-time Process Monitoring

Successfully manufacturing automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components.

The purpose of the project is to develop an AI-driven process control algorithm for stamping process. The project will first focus on the development of a prototype using a simplified product geometry, whose tool is referred to in this project as the Kidney Die. The purpose of initiating the development on this relatively simple geometry is to avoid complicating the matters by introducing tool shapes that might limit potential solutions. Phase 1 will focus on developing the overarching methodology for process modeling, data exchange, and artificial intelligence methods by focusing the entire project team on a common reference stamping (Kidney Die tool). Phase 2 will apply the newly developed methodology (from Phase 1) to multiple active stampings that are in production or being prototyped for production at the Participant vehicle original equipment manufacturers (OEMs).