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Monitoring method of weld penetration in laser keyhole welding irradiated by laser auxiliary illuminant
19
Citations
21
References
2020
Year
EngineeringFeature DetectionLaser ApplicationsLaser WeldingImage AnalysisWelding ProcessPattern RecognitionAutomotive ManufacturingModerate Penetration StatusEdge DetectionMachine VisionLaser Auxiliary IlluminantLaser Processing TechnologyWeld Pool SolidificationOptical Image RecognitionWeld PenetrationAutomated InspectionComputer VisionAdvanced Laser ProcessingLaser KeyholeLaser Damage
In automotive manufacturing, achieving moderate penetration in laser keyhole welding is critical, and keyhole behavior directly reflects weld penetration status. This study establishes a coaxial vision monitoring system with a laser auxiliary illuminant and a hybrid adaptive keyhole detection algorithm to capture and segment keyhole images during welding. The system captures images while mitigating bright‑spot interference, extracts five static image features, and applies a Wrapper‑based random‑forest feature selection to identify ten penetration‑status features. The resulting prediction model using these features demonstrates excellent performance and enables closed‑loop control to achieve moderate penetration.
In the field of automotive manufacturing, it is pivotal to obtain a moderate penetration status when joining automotive parts by laser keyhole welding. As a typical characteristic of laser keyhole welding, keyhole behavior can directly reflect the penetration status of the weld bead. In this paper, a coaxial vision monitoring system with a laser auxiliary illuminant is established to collect the keyhole images from the top-face of the weldment during laser welding. The interferences of the bright spot caused by mirror reflection and metal vapors in the collected images are fully considered. Then, a hybrid adaptive keyhole detection algorithm is proposed to accurately segment the keyhole region. Five static image features are extracted from the perspective of the average gray value, area, and perimeter in the keyhole region. The feature selection method of Wrapper, which combines a sequential forward searching algorithm with a random forest classifier, is used to select ten penetration status features (PSFs). The constructed prediction model of weld penetration based on the selected PSFs has excellent performance. The proposed monitoring method in laser keyhole welding irradiated by a laser auxiliary illuminant is contributed to the closed-loop control of weld penetration to obtain a moderate penetration status.
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