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Developing a numerical modelling strategy for metallic pin pressing processes in fibre reinforced thermoplastics to investigate fibre rearrangement mechanisms during joining

Gröger, B., Gerritzen, J., Hornig, A. & Gude, M., Dec 2024, In: Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications. 238, 12, p. 2286-2298 13 p.

Research output: Contribution to journal › Research article › Contributed › peer-review

Graph based process models as basis for efficient data driven surrogates - Expediting the material development process

Gerritzen, J., Hornig, A. & Gude, M., 16 Nov 2024, Open Engineering Inc., 16 p. ( engrXiv : engineering archive).

Research output: Preprint/Documentation/Report › Preprint

How to profit from ML in material development processes - An efficient way to data based surrogate models

Gerritzen, J., Hornig, A. & Gude, M., 16 Oct 2024.

Research output: Contribution to conferences › Presentation slides › Contributed

A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

Gerritzen, J., Hornig, A., Winkler, P. & Gude, M., Sept 2024, In: Computational Materials Science. 244 (2024), 10 p., 113274.

Research output: Contribution to journal › Research article › Contributed › peer-review

Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning

Gerritzen, J., Hornig, A., Winkler, P. & Gude, M., 2 Jul 2024, ECCM21 - Proceedings of the 21st European Conference on Composite Materials: Volume 3 - Material and Structural Behavior – Simulation & Testing. European Society for Composite Materials (ESCM), Vol. 3. p. 1252-1259 8 p.

Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review

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