Di Labbio, Giuseppe, Hof, Lucas, Chaouki, Haitam and 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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Abstract
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.
| Item Type: | Conference proceeding |
|---|---|
| Editors: | Editors ORCID Hof, Lucas A. UNSPECIFIED Di Labbio, Giuseppe UNSPECIFIED Tahan, Antoine UNSPECIFIED Sanjosé, Marlène UNSPECIFIED Lalonde, Sébastien UNSPECIFIED Demarquette, Nicole R. UNSPECIFIED Hof, Lucas UNSPECIFIED |
| Professor: | Professor Di Labbio, Giuseppe Hof, Lucas |
| Affiliation: | Génie mécanique, Génie mécanique |
| Date Deposited: | 18 Dec 2025 15:15 |
| Last Modified: | 18 Dec 2025 15:15 |
| URI: | https://espace2.etsmtl.ca/id/eprint/32440 |
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