{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:23:31Z","timestamp":1754155411228,"version":"3.41.2"},"reference-count":28,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2014,5,13]],"date-time":"2014-05-13T00:00:00Z","timestamp":1399939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,5,13]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 The purpose of this paper was to use visual and arc sensors to simultaneously obtain the underwater wet welding information, and a weld seam-forming model was made to predict the weld seam's geometric parameters. It is difficult to obtain a fine welding quality in underwater welding because of the intense disturbances of the water environment. To automatically control the welding quality, the weld seam-forming model should first be established. Thus, the foundation was laid for automatically controlling the underwater welding seam-forming quality. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 Visual and arc sensors were used simultaneously to obtain the weld seam image, current and voltage information; then signal algorithms were used to process the information, and the back propagation (BP) neural network was used to model the process. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 Experiment results showed that the BP neural network model could precisely predict the weld seam-forming parameters of underwater wet welding. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 A weld seam-forming model of underwater wet welding process was made; this laid the foundation for establishing a controller for controlling the underwater wet welding process automatically.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/ir-03-2014-0315","type":"journal-article","created":{"date-parts":[[2014,7,10]],"date-time":"2014-07-10T10:31:17Z","timestamp":1404988277000},"page":"311-317","source":"Crossref","is-referenced-by-count":11,"title":["Modeling of underwater wet welding process based on visual and arc sensor"],"prefix":"10.1108","volume":"41","author":[{"given":"Bo","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jicai","family":"Feng","sequence":"first","affiliation":[]}],"member":"140","reference":[{"key":"key2021010103140283000_b1","unstructured":"Aschemeier, U.\n (2008), \u201cThe American welder-underwater welder training from an engineer's prospective-an experienced welding engineer finds his venture into an underwater welding training program not quite as easy as expected\u201d, Welding Journal, Vol. 87 No. 11, p. -."},{"key":"key2021010103140283000_b2","unstructured":"Cao, Y.\n , \n Zhu, S.\n , \n Wang, T.\n and \n Wang, W.\n (2009), \u201cModeling of 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