Deployment of Machine Vision Platform for Checking Spot Welds on Metal Strap Belts

dc.contributor.authorTheerayod Wiangtong
dc.contributor.authorSiripong Wongkharn
dc.contributor.authorPhaophak Sirisuk
dc.date.accessioned2026-05-08T19:22:08Z
dc.date.issued2023-3-8
dc.description.abstractThis paper presents a designed platform used to detect the perfection and number of spot welds on the strap belt of metal sheet coils. Three different approaches include image morphology, thresholding and Hough transform are compared. The results from real implementation show that using the adaptive threshold values in image thresholding approach instead of fixed value can increase the system accuracy from 69% to 88%. Also, to find the pad, the comparison of using Haar cascade machine learning and YOLO deep learning is described.
dc.identifier.doi10.1109/ieecon56657.2023.10126530
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18394
dc.subjectIndustrial Vision Systems and Defect Detection
dc.subjectWelding Techniques and Residual Stresses
dc.subjectAdvanced Neural Network Applications
dc.titleDeployment of Machine Vision Platform for Checking Spot Welds on Metal Strap Belts
dc.typeArticle

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