[2603.20418] Data-driven discovery of roughness descriptors for surface characterization and intimate contact modeling of unidirectional composite tapes

Computer Science > Machine Learning arXiv:2603.20418 (cs) [Submitted on 20 Mar 2026] Title:Data-driven discovery of roughness descriptors for surface characterization and intimate contact modeling of unidirectional composite tapes Authors:Sebastian Rodriguez, Mikhael Tannous, Jad Mounayer, Camilo Cruz, Anais Barasinski, Francisco Chinesta View a PDF of the paper titled Data-driven discovery of roughness descriptors for surface characterization and intimate contact modeling of unidirectional composite tapes, by Sebastian Rodriguez and 4 other authors View PDF HTML (experimental) Abstract:Unidirectional tapes surface roughness determines the evolution of the degree of intimate contact required for ensuring the thermoplastic molecular diffusion and the associated inter-tapes consolidation during manufacturing of composite structures. However, usual characterization of rough surfaces relies on statistical descriptors that even if they are able to represent the surface topology, they are not necessarily connected with the physics occurring at the interface during inter-tape consolidation. Thus, a key research question could be formulated as follows: Which roughness descriptors simultaneously enable tape classification-crucial for process control-and consolidation modeling via the inference of the evolution of the degree of intimate contact, itself governed by the process parameters?. For providing a valuable response, we propose a novel strategy based on the use of Rank Reducti...