Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/48849
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dc.contributor.authorShah, H.N.M.-
dc.contributor.authorKamis, Z.-
dc.contributor.authorSulaiman, M.-
dc.contributor.authorRashid, M.Z.A.-
dc.contributor.authorAras, M.S.M.-
dc.contributor.authorAhmad, A.-
dc.date.accessioned2019-07-08T06:32:00Z-
dc.date.available2019-07-08T06:32:00Z-
dc.date.issued2019-07-
dc.identifier.issn0975-1033 (Online); 0379-5136 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/48849-
dc.description1164-1169en_US
dc.description.abstractThe goal of this paper is to study the characteristics of the butt joint imperfections with different types of joint shapes (curve, straight and tooth saw work piece) according to their class categories (good welds, excess welds, insufficient welds and no welds). The work piece is placed in a center position on the workbench. The distance between camera and workpiece is set as 300 mm during welding imperfections process and the entire work piece image is taken from the same distance to maintain the accuracy. The input feature vector is determined by feature co-occurrence matrix consisting of energy, correlation, homogeneity and contrast both no scaled and scaled by 0.5. Results show that no welds class categories exhibit higher homogeneity compared to the other class categories. This is because the homogeneity value depends on bright and dark parts of a certain size and also include some changes from dark to bright. Meanwhile, insufficient welds class categories produced larger contrast value, but good weld class categories recorded higher contrast value.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJMS Vol.48(07) [July 2019]en_US
dc.subjectCo-occurrence Matrixen_US
dc.subjectButt jointen_US
dc.subjectImperfections weldingen_US
dc.titleCharacteristics of butt welding imperfections joint using co-occurrence matrixen_US
dc.typeArticleen_US
Appears in Collections:IJMS Vol.48(07) [July 2019]

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