首页|Data from University of British Columbia Advance Knowledge in Robotics (Vision-b ased Seam Tracking for Gmaw Fillet Welding Based On Keypoint Detection Deep Lear ning Model)
Data from University of British Columbia Advance Knowledge in Robotics (Vision-b ased Seam Tracking for Gmaw Fillet Welding Based On Keypoint Detection Deep Lear ning Model)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Robotics is now availab le. According to news originating from Vancouver, Canada, by NewsRx corresponden ts, research stated, “Pre-programmed welding robots significantly improved the e fficiency and quality of the welds in large batch production. In small and mediu m batch production, the robots need appropriate sensors to perform well and adap t to the changes and uncertainties in a noisy welding environment.” Our news journalists obtained a quote from the research from the University of B ritish Columbia, “Vision-based sensors enabled by machine learning are making it possible to sense in process previously not measurable. One challenge is develo ping artificial intelligent models capable of real-time seam tracking, particula rly in fillet joints where visual analysis is hindered by non-perpendicular came ra angles and arc reflections. In this paper, we propose a vision system that en ables automated seam tracking with a collaborative robot. The vision-based deep learning classification model detects the tacks, where the seam is not visible. It is based on a keypoint detection deep learning model that addresses the chall enges in distorted and noisy images of fillet joints between the pipes and flang es during the real-time Gas Metal Arc Welding to track the location of the seam in non tack images. The system is optimized for real time seam tracking by propo sing the appropriate input image size. Multiple images and multiple points are a lso considered to provide a controllable signal of the location of the seam with less errors and outliers. Our proposed model can track the seam with more than 80 percent accuracy for errors less than 0.3 mm in fillet joints. The high accur acy of the proposed method would result in fewer flaws and defects and reduced r ework, resulting in significant cost saving in manufacturing.”
VancouverCanadaNorth and Central Ame ricaEmerging TechnologiesMachine LearningNano-robotRoboticsUniversity of British Columbia
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