FE-simulation of shear cutting for aluminium bars by considering the stress and temperature dependent effects in the plasticity and damage modelling

• Applicant: Professor Dr.-Ing. Bernd-Arno Behrens
• Subject Area: Primary Shaping and Reshaping Technology, Additive Manufacturing
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 538263864

The aim of this project is to model the shear cutting of aluminium bars using comprehensive elasticity, plasticity and damage modelling within FE-simulations. In this way, in-depth research is expected to gain profound insights into the interrelationships between process parameters and the resulting quality of the cut surface. The challenge is that three dimensional influences must not be neglected in the simulation of the cutting process from bar stock, as is the case in sheet metal forming. As the preliminary investigations have shown, complex three-dimensional stress states arise during the cutting process, which significantly influence the accumulation of damage. The influences arising from the stress states are also superimposed by a temperature gradient emanating from the shear zone due to heat release. This in turn has a significant effect on the mechanical damage properties in the aluminium material. In order to achieve these objectives, a comprehensive characterisation is planned. The alloys EN AW- 6082-T6 and EN AW-7075-T6, which are frequently used in automotive body construction, will be investigated. Based on the material characterisation, a thermo-mechanical material model for the digital twin of shear cutting processes of bulk parts will be developed. This includes a stress based plasticity and damage model. The model should be validated by experimental shear cutting tests and used for in-depth process analysis. The vision of the applicants lies in the technological establishment of shear cutting for aluminium bars as a time-saving, cost- and resource-efficient process, which will be made possible by simulation-supported analyses.

• DFG Programme: Research Grants
• Co-Investigator: Dr.-Ing. Kai Brunotte