Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89656
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dc.contributor.authorPandiyan, Vigneashwaraen
dc.contributor.authorTjahjowidodo, Tegoehen
dc.contributor.authorSamy, Meena Periyaen
dc.date.accessioned2018-12-19T08:26:04Zen
dc.date.accessioned2019-12-06T17:30:28Z-
dc.date.available2018-12-19T08:26:04Zen
dc.date.available2019-12-06T17:30:28Z-
dc.date.issued2016en
dc.identifier.citationPandiyan, V., Tjahjowidodo, T., & Samy, M. P. (2016). In-process surface roughness estimation model for compliant abrasive belt machining process. Procedia CIRP, 46, 254-257. doi:10.1016/j.procir.2016.03.126en
dc.identifier.issn2212-8271en
dc.identifier.urihttps://hdl.handle.net/10356/89656-
dc.description.abstractSurface roughness inspection in robotic abrasive belt machining process is an off-line operation which is time-consuming. An in-process multi-sensor integration technique comprising of force, accelerometer and acoustic emission sensor was developed to predict state of the surface roughness during machining. Time and frequency-domain features extracted from sensor signals were correlated with the corresponding surface roughness to train the Support vector machines (SVM's) in Matlab toolbox and a classification model was developed. Prediction accuracy of the classification model shows proposed in-process surface roughness recognition system can be integrated with abrasive belt machining process for capping lead-time and is reliable.en
dc.format.extent4 p.en
dc.language.isoenen
dc.relation.ispartofseriesProcedia CIRPen
dc.rights© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en
dc.subjectIn-process Measurementen
dc.subjectRoughnessen
dc.subjectDRNTU::Engineering::Mechanical engineeringen
dc.titleIn-process surface roughness estimation model for compliant abrasive belt machining processen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.identifier.doi10.1016/j.procir.2016.03.126en
dc.description.versionPublished versionen
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item.grantfulltextopen-
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