Preparation and properties of micro-arc oxidation ceramic coatings using machine vision technology
In this paper, a machine vision system is combined with non-immersion micro-arc oxidation (MAO) technology, using color as a visual feature, and automatically determining the MAO process through a self-developed color image segmentation program for HSV channel. The robotic arm accurately regulates t...
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Veröffentlicht in: | Materials chemistry and physics 2024-07, Vol.320, p.129427, Article 129427 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | In this paper, a machine vision system is combined with non-immersion micro-arc oxidation (MAO) technology, using color as a visual feature, and automatically determining the MAO process through a self-developed color image segmentation program for HSV channel. The robotic arm accurately regulates the machining position and duration. Dense ceramic oxide coatings of similar quality to the artificially prepared coatings were successfully prepared on aluminum (Al) surface. The surface morphology and elemental composition of the samples were characterized using scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) and X-ray diffraction (XRD). The performance of the coatings was tested through thickness tests, hardness tests, and roughness tests. The tribological properties were tested by friction experiments, and its corrosion resistance was tested by electrochemical corrosion tests. The results confirm the effectiveness of a machine vision system for MAO process control, which is capable of preparing high-quality coatings comparable to manual operations. This not only solves the need for precise film formation in different application scenarios, but also provides a more efficient, controllable and innovative solution for the MAO process.
•Using a robotic arm to manufacture ceramic coatings by partial micro-arc oxidation on the surface of 6061 aluminium alloy.•A non-immersion micro-arc oxidation technology based on machine vision.•Machine vision combined with image recognition technology. |
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ISSN: | 0254-0584 1879-3312 |
DOI: | 10.1016/j.matchemphys.2024.129427 |