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AI-driven modelling of electrostatic powder coating: Data collection

Di Labbio, Giuseppe, Hof, Lucas, Chaouki, Haitam et Lessard, Michel. 2025. « AI-driven modelling of electrostatic powder coating: Data collection ». In Proceedings of the CSME-CFDSC-CSR 2025 International Congress (Montreal, QC, Canada, May 25-28, 2025) Coll. « Progress in Canadian Mechanical Engineering », vol. 8.

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Résumé

Powder coating has become a popular surface finishing technique in many industries (e.g., home appliances, automotive parts, outdoor products) owing to its durability, corrosion resistance and low environmental impact. While powder recycling systems are often in place to capture non-deposited powder, waste can nonetheless be further reduced by optimizing the uniformity of the applied coating thickness. In this work, an experimental data collection approach is proposed to generate a high-quality database for the training of an AI-driven model of the distribution of electrostatic powder coating on flat surfaces. We propose a novel, scalable, low-cost automated coating thickness measurement system based on a microscopic incision tool, an open hardware CNC machine, a Raspberry Pi and the open source OpenCV image processing library. The system is capable of characterizing the coating thickness distribution of flat plates at a custom spatial resolution (as low as 0.1 mm) in a reasonable time with an accuracy of 2 µm. The proposed system can serve as a quality control and process optimization tool in an industrial workflow.

Type de document: Compte rendu de conférence
Éditeurs:
Éditeurs
ORCID
Hof, Lucas A.
NON SPÉCIFIÉ
Di Labbio, Giuseppe
NON SPÉCIFIÉ
Tahan, Antoine
NON SPÉCIFIÉ
Sanjosé, Marlène
NON SPÉCIFIÉ
Lalonde, Sébastien
NON SPÉCIFIÉ
Demarquette, Nicole R.
NON SPÉCIFIÉ
Hof, Lucas
NON SPÉCIFIÉ
Professeur:
Professeur
Di Labbio, Giuseppe
Hof, Lucas
Affiliation: Génie mécanique, Génie mécanique
Date de dépôt: 18 déc. 2025 15:15
Dernière modification: 18 déc. 2025 15:15
URI: https://espace2.etsmtl.ca/id/eprint/32440

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