Three projects approved
The working group of industrial research associations (AiF) has announced the approval of three research projects of the LFT: "Failure analysis under plane strain (plane strain)", "Production-related design of modified tool surfaces for low-lubrication deep drawing" and "Metamodel-based consideration of the process chain for mechanical joining of sheet metal components".
The aim of the "Failure analysis under plane strain (plane strain)" project is to improve the determination of characteristic values under plane strain for improved failure prediction in order to achieve a shift in the process limits to higher degrees of deformation and higher achievable drawing depths. The conventional characterization of the forming limit should be improved in such a way that the failure under plane strain can be better predicted in order to increase the quality of the input parameters for the component design.
In the project "Serial design of modified tool surfaces for low-lubricant deep drawing", the use of lubricants in sheet metal forming is being reduced in cooperation with the Chair of Design and CAD at the University of Bayreuth by coating the tools with amorphous carbon layers. This increases the sustainability and cost-effectiveness of sheet metal forming. The focus at the LFT is on researching the tribological behavior of novel amorphous carbon layers under varying load levels. In order to gain insights into the application behavior of the layers under application-related operating conditions, tool life tests are carried out from the strip and a large number of components are pressed.
As part of the project "Metamodel-based consideration of the process chain for the mechanical joining of sheet metal components", the continuous modeling of a process chain for the production of sheet metal components is carried out using meta models with the aim of identifying deviations and predicting component properties. The meta-models are formed on the basis of a broad numerical database and through the use of automated machine learning (AutoML). The analytical description of cross-process chain relationships represents the basis for the control of mechanical joining processes.