Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151209
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dc.contributor.authorPandiyan, Vigneashwaraen_US
dc.contributor.authorMurugan, Pushparajaen_US
dc.contributor.authorTjahjowidodo, Tegoehen_US
dc.contributor.authorCaesarendra, Wahyuen_US
dc.contributor.authorManyar, Omey Mohanen_US
dc.contributor.authorThen, David Jin Hongen_US
dc.date.accessioned2021-06-29T04:45:41Z-
dc.date.available2021-06-29T04:45:41Z-
dc.date.issued2019-
dc.identifier.citationPandiyan, V., Murugan, P., Tjahjowidodo, T., Caesarendra, W., Manyar, O. M. & Then, D. J. H. (2019). In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning. Robotics and Computer-Integrated Manufacturing, 57, 477-487. https://dx.doi.org/10.1016/j.rcim.2019.01.006en_US
dc.identifier.issn0736-5845en_US
dc.identifier.other0000-0003-0074-5101-
dc.identifier.other0000-0002-9784-4204-
dc.identifier.other0000-0002-4420-0894-
dc.identifier.urihttps://hdl.handle.net/10356/151209-
dc.description.abstractTransforming the manufacturing environment from manually operated production units to unsupervised robotic machining centres requires a presence of reliable in-process monitoring system. In this paper, we demonstrate a technique for automatic endpoint detection of weld seam removal in a robotic abrasive belt grinding process with the help of a vision system using deep learning. The paper presents the results of the first investigative stage of semantic segmentation of weld seam removal states using encoder-decoder convolutional neural networks (EDCNN). An experimental investigation using four different weld seam states on mild steel work coupon are trained using the VGG-16 network based on encoder-decoder architecture. The results demonstrate the potential of the developed vision based methodology as a tool for endpoint prediction of the weld seam removal in real time during a compliant abrasive belt grinding process. The prediction system based on semantic segmentation is able to monitor weld profile geometry evolution taking into account the varying belt grinding parameters during machining which will allow further process optimisation.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationM-RT1.1 M4061298en_US
dc.relation.ispartofRobotics and Computer-Integrated Manufacturingen_US
dc.rights© 2019 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleIn-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learningen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.researchRolls-Royce@NTU Corporate Laben_US
dc.identifier.doi10.1016/j.rcim.2019.01.006-
dc.identifier.scopus2-s2.0-85060115036-
dc.identifier.volume57en_US
dc.identifier.spage477en_US
dc.identifier.epage487en_US
dc.subject.keywordsAbrasive Belt Grindingen_US
dc.subject.keywordsDeep Learningen_US
dc.description.acknowledgementThis work was conducted within the Rolls-Royce@NTU Corporate Lab with support from the National Research Foundation (NRF) (Grant No. M-RT1.1 M4061298) Singapore under the Corp Lab@University Scheme.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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