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

2026-03-20

AI Summary

Traditional statistical descriptors of surface roughness struggle to elucidate the underlying physical mechanisms governing interlayer bonding in composite tape layup processes and fail to simultaneously satisfy the dual demands of process control classification and bonding evolution modeling. To address this, this work proposes a Rank-Reduced Autoencoder (RRAE), which uniquely integrates truncated singular value decomposition (SVD) into the latent space of an autoencoder. By enforcing a low-rank linear structure on the latent representation, the RRAE jointly optimizes surface topography reconstruction fidelity and downstream task performance. The resulting physically interpretable roughness descriptors exhibit both high-fidelity reconstruction capabilities and strong efficacy for classification and predictive modeling, thereby enabling accurate prediction of bonding evolution and effective support for manufacturing process control.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Knowledge Engineering

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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 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.

Problem

• surface roughness
• intimate contact
• composite tapes
• roughness descriptors
• consolidation modeling

Innovation

• Rank Reduction Autoencoder
• surface roughness descriptor
• intimate contact modeling
• singular value decomposition
• data-driven discovery