{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T11:19:56Z","timestamp":1683803996023},"reference-count":16,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2015,3,16]],"date-time":"2015-03-16T00:00:00Z","timestamp":1426464000000},"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":[[2015,3,16]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 This paper aim to build an information fusion model that can predict the bottom shape of welding groove for better welding quality control. Arc sensor is widely used in seam tracking due to its simplicity and good accessibility, but it heavily relies on the bottom shape of the groove. It is necessary to identify the welding groove bottom state. Therefore, arc sensor information and vision sensing information were fused by the rough set (RS) method to predict the groove state, which will lay the foundation for better welding quality control. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 First, a multi-sensor information system was established, which included an arc sensing component and a vision sensing component. For the arc sensing system, the current waveform in each rotating period was obtained and divided into 12 parts to calculate variables representing the variation of arc length. For the vision sensing system, images were obtained by passive vision when the arc was near the groove sidewall. The positions of the sidewall and the arc were calculated to get the weld deviation which was unrelated with the bottom groove state. Second, experimental data were generated by workpiece with various bottom shapes. At last, the RS method was adopted to fuse the arc sensing and the vision information, and a rule-based model with good prediction ability was obtained. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 By fusing arc sensing and vision sensing information, an RS-based model was built to predict the welding groove state. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 The RS modeling method was used to fuse arc sensing information and vision sensing information to build a model that predicts groove bottom state. The arc sensing information represented the arc length variation, while the vision sensing information contains the seam deviation which was unrelated with the bottom groove state. The RS model gives satisfactory prediction results and can be applied to weld quality control.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/ir-10-2014-0404","type":"journal-article","created":{"date-parts":[[2015,3,18]],"date-time":"2015-03-18T10:05:45Z","timestamp":1426673145000},"page":"110-116","source":"Crossref","is-referenced-by-count":4,"title":["Rough set based modeling for welding groove bottom state in narrow gap MAG welding"],"prefix":"10.1108","volume":"42","author":[{"given":"Wenhang","family":"Li","sequence":"first","affiliation":[]},{"given":"Jing","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020122600065763800_b1","doi-asserted-by":"crossref","unstructured":"Bae, K.Y.\n , \n Lee, T.H.\n and \n Ahn, K.C.\n (2002), \u201cAn optical sensing system for seam tracking and weld pool 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of multi-sensor information fusion in pulsed GTAW\u201d, \n Industrial Robot: An International Journal\n , Vol. 37 No. 2, pp. 168-176.","DOI":"10.1108\/01439911011018948"},{"key":"key2020122600065763800_b5","doi-asserted-by":"crossref","unstructured":"Chmielewski, M.R.\n and \n Grzymala-Busse, J.W.\n (1996), \u201cGlobal discretization of continuous attributes as preprocessing for machine learning\u201d, \n International Journal of Approximate Reasoning\n , Vol. 15 No. 4, pp. 319-331.","DOI":"10.1016\/S0888-613X(96)00074-6"},{"key":"key2020122600065763800_b6","doi-asserted-by":"crossref","unstructured":"Kim, G.H.\n and \n Na, S.J.\n (2001a), \u201cA study of an arc sensor model for gas metal arc welding with rotating arc Part 1: dynamic simulation of wire melting\u201d, \n Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture\n , Vol. 215 No. 9, pp. 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\n Wang, J.\n (2014), \u201cSVM-based information fusion for weld deviation extraction and weld groove state identification in rotating arc narrow gap MAG welding\u201d, \n International Journal of Advanced Manufacturing Technology\n , Vol. 74, pp. 1355-1364.","DOI":"10.1007\/s00170-014-6079-x"},{"key":"key2020122600065763800_b9","unstructured":"Li, W.H.\n , \n Sun, D.D.\n , \n Yang, F.\n and \n Wang, J.Y.\n (2011), \u201cModeling method for narrow gap MAG welding seam tracking based on rough sets\u201d, \n Materials Science and Technology\n , Vol. 19 No. 6, pp. 48-52."},{"key":"key2020122600065763800_b11","doi-asserted-by":"crossref","unstructured":"Moon, H.-S.\n , \n Kim, Y.B.\n and \n Beattie, R.J.\n (2006), \u201cMulti sensor data fusion for improving performance and reliability of fully automatic welding system\u201d, \n The International Journal of Advanced Manufacturing Technology\n , Vol. 28 No. 3, pp. 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