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Microwave Resin Preheating Integrated with AI-Based Flow Front Monitoring for Liquid Composite Molding Processes

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Abstract:

The impregnation represents a crucial phase in liquid composite molding (LCM) processes. Researchers over the years have used various approaches for monitoring, based on smart weave, pressure sensors, dielectric. Among the LCM processes, the vacuum bag allows the use of visual systems for detecting the resin flow front. The integration of monitoring systems with controllers for automated management of process parameters leads to an improvement in the characteristics of the final manufactured component. In the present work, an AI-based system integrated with the control of a resin preheating system allows for improvement of the impregnation stage. 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 in real time. The flow front position has been compared with the theoretical one, evaluated by using the Darcy’s law and based on the mismatch the controller suggests a proper in-time regulation of microwave power. The implemented system is capable of processing images through an AI-based algorithm and extracting the kinematic data of the flow front and integrating the information from the thermocouples and the visual system to control the microwave power.

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Periodical:

Materials Science Forum (Volume 1182)

Pages:

23-29

DOI:

https://doi.org/10.4028/p-ScfP7t

Citation:

Cite this paper

Online since:

April 2026

Authors:

Vitantonio Esperto*, Felice Rubino, Fausto Tucci, Pierpaolo Carlone

Keywords:

Composite, Digital Twin, In-Process Measurement, Machine Learning

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Creative Commons CC BY 4.0

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* - Corresponding Author

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