Development of a self-learning transformation and dilatometer model for the virtual process design of hot stamping processes

Funding period:

Start: 1. July 2019
Ende: 30. September 2021

Abstract

Partial hot stamping is a process variant to conventional hot stamping for manufacturing parts with tailored properties. In terms of a time and cost-efficient process design, an accurate prediction of the microstructural changes over the process chain is necessary. To minimize the experimental effort for this, a virtual dilatometer was developed within the framework of this research project. Therefore, an extensive database was set up. Based on the experimental data, an existing material model was extended. Together with a self-learning function for iterative experimental design, this is the main part of the virtual dilatometer. The developed process model was validated through a hot stamped demonstrator component.  

Research groups

• Materials Characterization and Modeling

Publications