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

TL;DR

This paper introduces a novel data-driven approach using Rank Reduction Autoencoders with SVD to derive meaningful roughness descriptors for surface characterization and modeling of unidirectional composite tapes, linking surface topology with interface physics.

Contribution

It proposes a new method employing RRAEs with SVD to extract physically relevant roughness descriptors that improve classification and modeling of tape surface contact.

Findings

Effective extraction of roughness descriptors correlating with interface physics

Enhanced tape classification accuracy for process control

Improved modeling of intimate contact evolution during manufacturing

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.