CRC/Transregio 188 – Scientific service project S01 - IUL

The scientific service project S01 supports and coordinates collaboration among the TRR188, with a particular focus on the implementation, calibration, and validation of damage models.

Funding and contact

Funding	DFG
Project	TRR188: Damage-controlled forming processes /project number 278868966
Project Partners	https://trr188.de/
Contact	Jan Gerlach M.Sc.
Status	On going

Project description:

In the 1st funding period (FP), established local damage models were implemented into the commercial software Abaqus and calibrated using experimental data. In the 2nd FP, the open-source software tool ADAPT for inverse parameter identification of constitutive material models was developed and published on GitHub. This tool enabled the model calibration for TRR 188 materials DP800 and 16MnCrS5. Furthermore, a model for the evolution of ductile damage in the sense of void fractions using artificial neural networks was proposed. In contrast to constitutive damage models, the damage prediction is solely based on experimental data and has no underlying assumptions for the damage evolution law. In the 3rd FP, this framework will be extended to non-monotonic loading paths, with physical augmentation via a thermodynamically consistent neural network. The model for the damage evolution will be linked to elastic and plastic properties to quantify void effects on macroscopic product properties. Damage model calibration remains a key objective, with uncertainty quantification applied to assess parameter sensitivity with respect to deviations in the experimental input data.

Publications:

Gerlach, J., Schulte, R., Schowtjak, A., Clausmeyer, T., Ostwald, R., Tekkaya, A.E., Menzel, A., 2024.

Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification. Archive of applied mechanics 94. https://doi.org/10.1007/s00419-024-02634-1

Langenfeld, K., Lingnau, L.A., Gerlach, J., Kurzeja, P., Gitschel, R., Walther, F., Kaiser, T., Clausmeyer, T., 2023.

Low cycle fatigue of components manufactured by rod extrusion: experiments and modeling. Advances in industrial and manufacturing engineering 7. https://doi.org/10.1016/j.aime.2023.100130

Gerlach, J., Clausmeyer, T., Schowtjak, A., Muhammad, W., Brahme, A.P., Koppka, L., Inal, K., Tekkaya, A.E., 2023.

Data-driven ductile damage model for damage-induced material degradation in forming. Manufacturing letters 35. https://doi.org/10.1016/j.mfglet.2023.08.092

Schowtjak, A., Gerlach, J., Muhammad, W., Brahme, A.P., Clausmeyer, T., Inal, K., Tekkaya, A.E., 2022.

Prediction of ductile damage evolution based on experimental data using artificial neural networks. International journal of solids and structures 257. https://doi.org/10.1016/j.ijsolstr.2022.111950

Schowtjak, A., Schulte, R., Clausmeyer, T., Ostwald, R., Tekkaya, A.E., Menzel, A., 2022.

ADAPT — a diversely applicable parameter identification tool: overview and full-field application examples. International journal of mechanical sciences 213. https://doi.org/10.1016/j.ijmecsci.2021.106840