Cycle-consistent generative adversarial networks for damage evolution analysis in fiber-reinforced polymers based on synthetic damage states

Helwing, Ramon; Mrzljak, Selim; Hülsbusch, Daniel; Walther, Frank

doi:10.1016/j.compscitech.2024.110695

Composites Science and Technology

2024

DOI: 10.1016/j.compscitech.2024.110695

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Cycle-consistent generative adversarial networks for damage evolution analysis in fiber-reinforced polymers based on synthetic damage states

Ramon Helwing,

Selim Mrzljak,

Daniel Hülsbusch

et al.

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Cycle-consistent generative adversarial networks for damage evolution analysis in fiber-reinforced polymers based on synthetic damage states

Cited by 1 publication

References 15 publications

An improved automatic image labeling and classification algorithm for multi-mode damage quantification of 2.5D woven composites based on deep learning strategy

An improved automatic image labeling and classification algorithm for multi-mode damage quantification of 2.5D woven composites based on deep learning strategy

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