Development of a self-learning transformation and dilatometer model for the virtual process design of hot stamping processes
Third party funded individual grant
Start date : 01.07.2019
End date : 30.09.2021
Project details
Scientific 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.