An AI-based approach for flow front monitoring and prediction in liquid composite molding processes based on dielectric and visual data elaboration
Vitantonio ESPERTO, Fausto TUCCI, Felice RUBINO, Pierpaolo CARLONE
Abstract. Liquid composite molding processes involve the impregnation and saturation of a dry preform by a liquid reactive resin, driven by pressure gradients. The correct advance of the resin flow front during the infusion is crucial to achieve defect-free composite products. In the present work, monitoring architecture based on real-time acquisition of data provided by dielectric and visual sensors has been developed. A machine learning approach, based on the You Only Look Once (YOLO) algorithm, has been integrated with the visual monitoring system to detect and dynamically track the resin flow front, deriving relevant process parameters in real-time. The data obtained highlights the effectiveness of the combined monitoring strategy and proposes a sensing tool for the further study of impregnation and saturation phenomena in liquid composite processing.
Keywords
Neural Network, Machine Learning, Composites, Image Processing, In-Process Measurement, Predictive Model, Vacuum-Bag Molding
Published online 9/10/2025, 8 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Vitantonio ESPERTO, Fausto TUCCI, Felice RUBINO, Pierpaolo CARLONE, An AI-based approach for flow front monitoring and prediction in liquid composite molding processes based on dielectric and visual data elaboration, Materials Research Proceedings, Vol. 57, pp 369-376, 2025
DOI: https://doi.org/10.21741/9781644903735-43
The article was published as article 43 of the book Italian Manufacturing Association Conference
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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