Experimental data and FE-model for tensile tests of DC06 at high strain rates In high strain rate forming processes two superposing and opposing effects influence the flow stress of the material: strain rate hardening and thermal softening due to adiabatic heating. The presented FE-model and experimental results are based on https://doi.org/10.3390/app12052299 where uniaxial tensile tests at different high strain rates are analyzed experimentally and numerically to understand the influence of adiabatic heating of the workpiece during deformation under high-speed loading. A thermal camera and a pyrometer were used for temperature measurement in the fracture region in addition to the measurement of force and elongation. The numerical simulations are carried out in LS-Dyna using the GISSMO model for modeling damage and failure. https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5833 2026-04-12T12:56:52Z 2026-04-12T12:56:52Z Forming at high strain rates Part 2: Analysis of adiabatic heating based on tensile tests of DC06 Tulke, Marc Galiev, Elmar https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5983 2023-10-27T03:30:17Z 2023-01-01T00:00:00Z Forming at high strain rates Part 2: Analysis of adiabatic heating based on tensile tests of DC06 Tulke, Marc; Galiev, Elmar In high strain rate forming processes two superposing and opposing effects influence the flow stress of the material: strain rate hardening and thermal softening due to adiabatic heating. The presented FE-model and experimental results are based on https://doi.org/10.3390/app12052299 where uniaxial tensile tests at different high strain rates are analyzed experimentally and numerically to understand the influence of adiabatic heating of the workpiece during deformation under high-speed loading. A thermal camera and a pyrometer were used for temperature measurement in the fracture region in addition to the measurement of force and elongation. The numerical simulations are carried out in LS-Dyna using the GISSMO model for modeling damage and failure. The used inverse parameter identification is first presented in https://doi.org/10.3390/jmmp4020031 and the associated data is published in http://dx.doi.org/10.25532/OPARA-246. 2023-01-01T00:00:00Z