{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:23:48Z","timestamp":1754155428823,"version":"3.41.2"},"reference-count":27,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2023,2,17]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The traditional vision system cannot automatically adjust the feature point extraction method according to the type of welding seam. In addition, the robot cannot self-correct the laying position error or machining error. To solve this problem, this paper aims to propose a hierarchical visual model to achieve automatic arc welding guidance.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The hierarchical visual model proposed in this paper is divided into two layers: welding seam classification layer and feature point extraction layer. In the welding seam classification layer, the SegNet network model is trained to identify the welding seam type, and the prediction mask is obtained to segment the corresponding point clouds. In the feature point extraction layer, the scanning path is determined by the point cloud obtained from the upper layer to correct laying position error. The feature points extraction method is automatically determined to correct machining error based on the type of welding seam. Furthermore, the corresponding specific method to extract the feature points for each type of welding seam is proposed. The proposed visual model is experimentally validated, and the feature points extraction results as well as seam tracking error are finally analyzed.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The experimental results show that the algorithm can well accomplish welding seam classification, feature points extraction and seam tracking with high precision. The prediction mask accuracy is above 90% for three types of welding seam. The proposed feature points extraction method for each type of welding seam can achieve sub-pixel feature extraction. For the three types of welding seam, the maximum seam tracking error is 0.33\u20130.41\u2009mm, and the average seam tracking error is 0.11\u20130.22\u2009mm.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The main innovation of this paper is that a hierarchical visual model for robotic arc welding is proposed, which is suitable for various types of welding seam. The proposed visual model well achieves welding seam classification, feature point extraction and error correction, which improves the automation level of robot welding.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-05-2022-0127","type":"journal-article","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T06:53:51Z","timestamp":1666421631000},"page":"299-313","source":"Crossref","is-referenced-by-count":6,"title":["A hierarchical visual model for robot automatic arc welding guidance"],"prefix":"10.1108","volume":"50","author":[{"given":"Chen","family":"Chen","sequence":"first","affiliation":[]},{"given":"Tingyang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhenhua","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Chunnian","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Xiaoyue","family":"Jin","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2022,10,25]]},"reference":[{"issue":"12","key":"key2023021612331854000_ref001","first-page":"2481","article-title":"Segnet: a deep convolutional encoder-decoder architecture for image segmentation","volume":"39","year":"2017","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"1\/2","key":"key2023021612331854000_ref002","first-page":"251","article-title":"Precise seam tracking in robotic welding by an improved image processing approach","volume":"114","year":"2021","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"9\/12","key":"key2023021612331854000_ref004","first-page":"1849","article-title":"A robust visual servo control system for narrow seam double head welding robot","volume":"71","year":"2014","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"key2023021612331854000_ref003","doi-asserted-by":"crossref","first-page":"100144","DOI":"10.1016\/j.jii.2020.100144","article-title":"Pavement crack detection and recognition using the architecture of segNet","volume":"18","year":"2020","journal-title":"Journal of Industrial Information Integration"},{"issue":"7","key":"key2023021612331854000_ref005","doi-asserted-by":"crossref","first-page":"8457","DOI":"10.1007\/s13369-021-06301-3","article-title":"A low-cost vision system using a retrofitted robot for locating parts for welding process","volume":"47","year":"2022","journal-title":"Arabian Journal for Science and Engineering"},{"first-page":"1889","article-title":"A low cost hand-eye calibration method for arc welding robots","year":"2009","key":"key2023021612331854000_ref006"},{"issue":"1\/4","key":"key2023021612331854000_ref007","first-page":"989","article-title":"Automatic recognition system of welding seam type based on SVM method","volume":"92","year":"2017","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"first-page":"6127","article-title":"A simple calibration method of structured light plane parameters for welding robots","year":"2016","key":"key2023021612331854000_ref008"},{"key":"key2023021612331854000_ref009","unstructured":"Gong, G. 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