Photonic sensor-based machine learning for precise forecasting of cure time and temperature overshoot in resin transfer moulding

Evrydiki Kyriazi; Georgios Petsinis; Charalampos Zervos; Giannis Poulopoulos; Georgios Syriopoulos; Geoffrey Neale; Mehdi Asareh; Alexandros A. Skordos; Hercules Avramopoulos

doi:10.1117/12.3001532

11 March 2024 Photonic sensor-based machine learning for precise forecasting of cure time and temperature overshoot in resin transfer moulding

Evrydiki Kyriazi, Georgios Petsinis, Charalampos Zervos, Giannis Poulopoulos, Georgios Syriopoulos, Geoffrey Neale, Mehdi Asareh, Alexandros A. Skordos, Hercules Avramopoulos

Author Affiliations +

Proceedings Volume 12893, Photonic Instrumentation Engineering XI; 1289318 (2024) https://doi.org/10.1117/12.3001532
Event: SPIE OPTO, 2024, San Francisco, California, United States

Conference Poster

Abstract

Fibre thermosetting composites play a major role in the engineering of advanced structures due to their combination of light weight and high strength and stiffness as well as the design flexibility. The high manufacturing cost and the inherently low production rates are the main limiting factors in increasing adoption of composites which can be overcome through the development of manufacturing strategies, materials and methodologies of process optimization and control. An accurate estimation of the stage of cure of thermosetting composites production is critical to deduce the overall process duration and ultimately the manufacturing costs. Challenges arise due to temperature overshoots and lack of direct measurement and control of the cure stage, particularly in thick components where the effects of the exothermic nature of the curing reaction and composite low thermal conductivity are more pronounced. To address these challenges and enabling the real-time process optimization, this study proposes a novel approach based on a machine learning (ML) model using simulation Finite Element Method (FEM) data as well as PIC-based photonic sensors realized on Silicon-on-Insulator (SOI) platform. Two robust Voting regressors, XGBoost and Light Gradient Boosting Machine, are used in the model to accurately (98% accuracy) predict two critical parameters: Cure time and Temperature Overshoot. Using photonic sensors to monitor the process in real time, we present experimental validation of Overshoot on manufacture RTM-6 aerospace composite parts.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Evrydiki Kyriazi, Georgios Petsinis, Charalampos Zervos, Giannis Poulopoulos, Georgios Syriopoulos, Geoffrey Neale, Mehdi Asareh, Alexandros A. Skordos, and Hercules Avramopoulos "Photonic sensor-based machine learning for precise forecasting of cure time and temperature overshoot in resin transfer moulding", Proc. SPIE 12893, Photonic Instrumentation Engineering XI, 1289318 (11 March 2024); https://doi.org/10.1117/12.3001532

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