Vallaster E, Wiesenmayer S, Merklein M (2023)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2023
Open Access Link: https://doi.org/10.1007/s11740-023-01222-6
In the production of sheet metal components, batch and process fluctuations cause deviations in the resulting component properties, which often lead to production rejects. To counteract this inline, the computing time for predicting the process result and optimizing the process parameters must be very short, which is why analytical models are advantageous. A large database is usually required for modeling, and numerical simulations are well suited for generating it. The stamping velocity is a process parameter possibly varying, but strain rate dependency of the material often is neglected in numerical simulations. The objective of this study is to analyze the effects of strain rate dependent material modeling on the simulation accuracy of a sheet metal forming process. Therefore, uniaxial tensile tests and layer compression tests at different strain rates are conducted on the steel HC340LA. Based on this, the material behavior is captured in a strain rate dependent material card, which is used for the numerical simulation of a deep drawing process of a geometry with complex shape. For the validation of the model, experiments are carried out and being compared with the computational results in terms of force–displacement curves and part geometry. Furthermore, numerical investigations are used to analyze if drawbead height and blankholder force have an influence on the strain rate distribution and whether this affects the process force.
Vallaster, E., Wiesenmayer, S., & Merklein, M. (2023). Effect of a strain rate dependent material modeling of a steel on the prediction accuracy of a numerical deep drawing process. Production Engineering. https://dx.doi.org/10.1007/s11740-023-01222-6
Vallaster, Eva, Sebastian Wiesenmayer, and Marion Merklein. "Effect of a strain rate dependent material modeling of a steel on the prediction accuracy of a numerical deep drawing process." Production Engineering (2023).