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

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 Reduction Autoencoders (RRAEs), autoencoders with a linear latent vector space enforced by applying a truncated Singular Value Decomposition (SVD) to the latent matrix during the encoder-decoder training. In this work, we extract useful roughness descriptors by enforcing the latent SVD modes to (i) accurately represent the roughness after decoding, and (ii) allow the extraction of existing a priori knowledge such as classification or modelling properties. • Data-driven framework based on a Rank Reduction AutoEncoder (RRAE) to extract latent descriptors of surface roughness in unidirectional composite tapes. • A limited number of features enables accurate roughness reconstruction, surface classification, and prediction of intimate contact evolution. • Improved robustness and accuracy compared to standard machine learning and statistical approaches. • Strong potential for process optimization and real-time control in composite forming.