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Rate dependent non-linear mechanical behaviour of continuous fibre-reinforced thermoplastic composites--Experimental characterisation and viscoelastic-plastic damage modelling

M. Zscheyge, R. Boehm, A. Hornig, J. Gerritzen, and M. Gude. Materials & Design, (2020)

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Application of Machine Learning Methods on the Defect Detection in Shearographic ImagesC. Düreth, A. Hornig, I. Koch, and M. Gude. (December 2022)Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networksV. Scholz, P. Winkler, A. Hornig, M. Gude, and A. Filippatos. Sensors (Basel), 21 (6): 2005 (March 2021)Rate dependent non-linear mechanical behaviour of continuous fibre-reinforced thermoplastic composites--Experimental characterisation and viscoelastic-plastic damage modellingM. Zscheyge, R. Boehm, A. Hornig, J. Gerritzen, and M. Gude. Materials & Design, (2020)A methodology for direct parameter identification for experimental results using machine learning—Real world application to the highly non-linear deformation behavior of FRPJ. Gerritzen, A. Hornig, P. Winkler, and M. Gude. Computational Materials Science, (2024)A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling ParametersJ. Gerritzen, A. Hornig, B. Gröger, and M. Gude. Journal of Composites Science, 6 (10): 318 (2022)

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