{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:48:33Z","timestamp":1778896113730,"version":"3.51.4"},"reference-count":15,"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 The purpose of this paper was to propose a method based on an Artificial Neural Network and a real-time vision algorithm, to learn welding skills in industrial robotics. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 By using an optic camera to measure the bead geometry (width and height), the authors propose a real-time computer vision algorithm to extract training patterns and to enable an industrial robot to acquire and learn autonomously the welding skill. To test the approach, an industrial KUKA robot and a welding gas metal arc welding machine were used in a manufacturing cell. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 Several data analyses are described, showing empirically that industrial robots can acquire the skill even if the specific welding parameters are unknown. <\/jats:p>\n <\/jats:sec>\n \n Research limitations\/implications<\/jats:title>\n \u2013 The approach considers only stringer beads. Weave bead and bead penetration are not considered. <\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n \u2013 With the proposed approach, it is possible to learn specific welding parameters despite of the material, type of robot or welding machine. This is due to the fact that the feedback system produces automatic measurements that are labelled prior to the learning process. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 The main contribution is that the complex learning process is reduced into an input-process-output system, where the process part is learnt automatically without human supervision, by registering the patterns with an automatically calibrated vision system.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/ir-09-2014-0395","type":"journal-article","created":{"date-parts":[[2015,3,18]],"date-time":"2015-03-18T06:05:45Z","timestamp":1426658745000},"page":"156-166","source":"Crossref","is-referenced-by-count":19,"title":["Acquisition of welding skills in industrial robots"],"prefix":"10.1108","volume":"42","author":[{"given":"J.F.","family":"Aviles-Vi\u00f1as","sequence":"first","affiliation":[]},{"given":"I.","family":"Lopez-Juarez","sequence":"additional","affiliation":[]},{"given":"R.","family":"Rios-Cabrera","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020122601124456900_b1","doi-asserted-by":"crossref","unstructured":"Chan, B.\n , \n Pacey, J.\n and \n Bibby, M.\n (1999), \u201cModelling gas metal arc weld geometry using artificial neural network technology\u201d, \n Canadian Metallurgical Quarterly\n , Vol. 38 No. 1, pp. 43-51.","DOI":"10.1179\/cmq.1999.38.1.43"},{"key":"key2020122601124456900_b2","unstructured":"\u010cudina, M.\n , \n Prezelj, J.\n and \n Polajnar, I.\n (2008), \u201cUse of audible sound for on-line monitoring of gas metal arc welding process\u201d, \n Metalurgia\n , Vol. 47 No. 2, pp. 81-85."},{"key":"key2020122601124456900_b7","doi-asserted-by":"crossref","unstructured":"Ganjigatti, J.P.\n , \n Pratihar, D.K.\n and \n Choudhury, A.R.\n (2008), \u201cModeling of the MIG welding process using statistical approaches\u201d, \n The International Journal of Advanced Manufacturing Technology\n , Vol. 35, pp. 1166-1190.","DOI":"10.1007\/s00170-006-0798-6"},{"key":"key2020122601124456900_b11","unstructured":"Gorle, P.\n and \n Clive, A.\n (2013), \n Positive Impact of Industrial Robots on Employment\n , International Federation of Robotics, METRA MARTECH Limited."},{"key":"key2020122601124456900_b4","doi-asserted-by":"crossref","unstructured":"Grossberg, S.\n , \n Markuzon, N.\n , \n Reynolds, J.H.\n , \n Carpenter, G.A.\n and \n Rosen, D.B.\n (1992), \u201cFuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps\u201d, \n IEEE Transactions on Neural Networks\n , Vol. 3 No. 5.","DOI":"10.1109\/72.159059"},{"key":"key2020122601124456900_b17","unstructured":"H\u00e4rdle, W.\n and \n Steiger, W.\n (1995), \u201cOptimal median smoothing\u201d, \n Journal of the Royal Statistical Society\n , Series C (Applied Statistics), Vol. 44, pp. 258-264."},{"key":"key2020122601124456900_b8","doi-asserted-by":"crossref","unstructured":"Horvat, J.\n , \n Prezelj, J.\n , \n Polajnar, I.\n and \n \u010cudina, M.\n (2011), \u201cMonitoring gas metal arc welding process by using audible sound signal\u201d, \n Strojni\u0161ki vestnik-Journal of Mechanical Engineering\n , Vol. 57 No. 3, pp. 267-278.","DOI":"10.5545\/sv-jme.2010.181"},{"key":"key2020122601124456900_b3","unstructured":"Kolahan, F.\n and \n Heidari, M.\n (2010), \u201cA new approach for predicting and optimizing weld bead geometry in GMAW\u201d, \n International Journal of Mechanical Systems Science and Engineering\n , Vol. 2 No. 2, pp. 138-142."},{"key":"key2020122601124456900_b5","unstructured":"Luo and Chen, X.Q.\n (2005), \u201cLaser visual sensing for seam tracking in robotic arc welding of titanium alloys\u201d, \n The International Journal of Advanced Manufacturing Technology\n , Vol. 26 Nos 9\/10, pp. 1012-1017."},{"key":"key2020122601124456900_b10","doi-asserted-by":"crossref","unstructured":"Lv, N.\n , \n Xu, Y.\n , \n Zhong, J.\n and \n Chen, H.\n (2013), \u201cResearch on detection of welding penetration state during robotic GTAW process based on audible arc sound\u201d, \n Industrial Robot: An International Journal\n , Vol. 40 No. 5, pp. 474-493.","DOI":"10.1108\/IR-09-2012-417"},{"key":"key2020122601124456900_b6","doi-asserted-by":"crossref","unstructured":"Ma, H.\n , \n Wei, S.\n , \n Lin, T.\n , \n Chen, S.\n and \n Li, L.\n (2010), \u201cBinocular vision system for both weld pool and root gap in robot welding process\u201d, \n Sensor Review\n , Vol. 30 No. 2, pp. 116-123.","DOI":"10.1108\/02602281011022706"},{"key":"key2020122601124456900_b12","unstructured":"Sreeraj, P.\n , \n Kannan, T.\n and \n Maji, S.\n (2013), \u201cPrediction and control of weld bead geometry in gas metal arc welding process using simulated annealing algorithm\u201d, \n International Journal of Computational Engineering Research\n , Vol. 3 No. 1, pp. 213-222."},{"key":"key2020122601124456900_b14","unstructured":"World Robotics\n (2013), \n Industrial Robots\n , International Federation of Robotics, IFR."},{"key":"key2020122601124456900_frd1","unstructured":"Hardlet, W.\n and \n Steiger, W.\n (1995), \u201cAlgorithm AS 296: optimal median smoothing\u201d, \n Applied Statistics\n , Vol. 44 No. 2, p. 258."},{"key":"key2020122601124456900_frd0","doi-asserted-by":"crossref","unstructured":"Kim, I.S.\n , \n Kwon, W.H.\n and \n Siores, E.\n (1996), \u201cAn investigation of a mathematical model for predicting weld bead geometry\u201d, \n Canadian Metallurgical Quarterly\n , Vol. 35 No. 4, pp. 385-392.","DOI":"10.1179\/cmq.1996.35.4.385"}],"container-title":["Industrial Robot: An International 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