Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/325405
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dc.date.accessioned2021-05-11T12:09:43Z-
dc.date.available2021-05-11T12:09:43Z-
dc.identifier.urihttp://hdl.handle.net/10603/325405-
dc.description.abstractWelding is one of the major metals joining processes employed in fabrication newlineindustries especially in the manufacturing of boilers and pressure vessels. Quality of newlineweld is an important aspect for such industries, considering the severe operating newlineconditions. Industries are looking forward for some kind of a real-time process newlinemonitoring/control using sensors and signal processing systems that will ensure the newlinerequired weld quality. In this work, an attempt is made to establish a correlation newlinebetween the current and voltage signatures with undisturbed arc condition producing newlinegood weld and disturbed arc condition producing porous and burn-through welds in a newlinerobotic pulsed Gas Metal Arc Welding (GMAW) of carbon steel plates used in boiler newlineapplication. Experiential studies are carried out to establish undisturbed and disturbed arc newlineconditions producing good weld and defective weld conditions respectively. newlineUndisturbed arc is established by following good weld practices established as per newlineWelding Procedure Specification (WPS). Disturbed arc conditions are intentionally newlineestablished by creating porosity and burn-through weld defects using experimental newlinedesigns. An experimental set-up is established to acquire current and voltage newlinesignatures of the undisturbed arc producing good weld and disturbed arc conditions newlineproducing burn-through and porosity welds. The behaviour of undisturbed and newlinedisturbed arc conditions are studied by analyzing the pulse parameters of current and newlinevoltage. Weld arc signatures and their correlation with good weld and defective weld newlineconditions are studied using Probability Density Distribution (PDD) plots of current newlineand voltage. Machine learning algorithms are used to build statistical models to newlineclassify the good weld and defective weld conditions. Statistical features extracted newlinefrom time, frequency and wavelet domains of current and voltage transients are used newlineto train and test the statistical models proposed in this study. Machine learning newlineclassifiers viz. decision trees, support vector machines ...
dc.format.extentxxvii, 244
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleArc Signature Classification using Machine Learning Approach to Identify Weld Defect Conditions in a Robotic Pulsed Gmaw Process
dc.title.alternative
dc.creator.researcherSumesh A
dc.subject.keywordElectric welding, Confusion Matrix ,Shielded Metal Arc Welding - SMAW, Gas metal arc welding ,arc welding ,Weld Quality, Weld Defects, Burn-Through , Weld Quality Monitoring ,Lack of Fusion, Current and voltage signature, Undisturbed arc, Disturbed arc, root gap, Vector Machines, neural network, Machine learning.
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Mechanical
dc.description.note
dc.contributor.guideRameshkumar K
dc.publisher.placeCoimbatore
dc.publisher.universityAmrita Vishwa Vidyapeetham University
dc.publisher.institutionDept. of Mechanical Engineering
dc.date.registered2010
dc.date.completed2019
dc.date.awarded2019
dc.format.dimensions
dc.format.accompanyingmaterialCD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Mechanical Engineering (Amrita School of Engineering)

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01_title.pdfAttached File171.05 kBAdobe PDFView/Open
02_certificate.pdf270.54 kBAdobe PDFView/Open
03_declaration.pdf240.79 kBAdobe PDFView/Open
04_dedicated.pdf140.51 kBAdobe PDFView/Open
05_acknowledgement.pdf354.49 kBAdobe PDFView/Open
06_abstract.pdf243.79 kBAdobe PDFView/Open
07_list of figure.pdf286.23 kBAdobe PDFView/Open
08_list of tables.pdf275.29 kBAdobe PDFView/Open
09_list of acronyms.pdf143.9 kBAdobe PDFView/Open
10_contents.pdf390.97 kBAdobe PDFView/Open
11_chapter 1.pdf815.02 kBAdobe PDFView/Open
12_chapter 2.pdf324.09 kBAdobe PDFView/Open
13_chapter 3.pdf426.51 kBAdobe PDFView/Open
14_chapter 4.pdf1.32 MBAdobe PDFView/Open
15_chapter 5.pdf1.42 MBAdobe PDFView/Open
16_chapter 6.pdf608.56 kBAdobe PDFView/Open
17_chapter7.pdf823.14 kBAdobe PDFView/Open
18_chapter 8.pdf1.29 MBAdobe PDFView/Open
19_chapter 9.pdf968.25 kBAdobe PDFView/Open
20_chapter 10.pdf1.74 MBAdobe PDFView/Open
21_chapter 11.pdf499.02 kBAdobe PDFView/Open
22_chapter 12.pdf440.08 kBAdobe PDFView/Open
23_chapter 13.pdf163.09 kBAdobe PDFView/Open
24_references.pdf286.47 kBAdobe PDFView/Open
25_publications.pdf128.43 kBAdobe PDFView/Open
80_recommendation.pdf333.7 kBAdobe PDFView/Open


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