Weldment defect detection and classification in ultrasonic testing using artificial intelligence

dc.contributor.guideNagaraj, Pen_US
dc.coverage.spatialMechanical Engineeringen_US
dc.creator.researcherSambath, Sen_US
dc.date.accessioned2014-10-08T06:25:38Z
dc.date.available2014-10-08T06:25:38Z
dc.date.awarded30/06/2010en_US
dc.date.completed01/06/2010en_US
dc.date.issued2014-10-08
dc.date.registeredn.d.en_US
dc.description.abstractUltrasound based inspection techniques are used extensively newlinethroughout industry for detection of flaws in engineering materials The newlineprincipal goal for ultrasonic inspection of engineering materials is the newlinedetection location and classification of internal flaws and defects as quickly newlineand as accurately as possible However this non destructive testing process is newlineoften difficult and time consuming and may well rely significantly on the skill newlineand experience of the tester The combination of the human eye and brain is newlineuniquely capable after training of classifying a wide range and variety of newlinecomplex patterns Performance is however subject to significant variation as a newlineresult of factors such as fatigue and loss of concentration newline newlineen_US
dc.description.noteReference p.174-183en_US
dc.format.accompanyingmaterialNoneen_US
dc.format.dimensions23cmen_US
dc.format.extentxxii,185p.en_US
dc.identifier.urihttp://hdl.handle.net/10603/26360
dc.languageEnglishen_US
dc.publisher.institutionFaculty of Mechanical Engineeringen_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.relation-en_US
dc.rightsuniversityen_US
dc.source.universityUniversityen_US
dc.subject.keywordartificial intelligenceen_US
dc.subject.keywordmechanical engineeringen_US
dc.subject.keywordultrasonic testingen_US
dc.titleWeldment defect detection and classification in ultrasonic testing using artificial intelligenceen_US
dc.title.alternative-en_US
dc.type.degreePh.D.en_US

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