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dc.contributor.authorRogge, Matthew Douglasen_US
dc.date.accessioned2010-01-29T19:42:48Z
dc.date.available2010-01-29T19:42:48Z
dc.date.issued2009-11-16en_US
dc.identifier.urihttp://hdl.handle.net/1853/31698
dc.description.abstractGas Metal Arc Welding (GMAW) is one of the main methods used to join structural members. One of the largest challenges involved in production of welds is ensuring the quality of the weld. One of the main factors attributing to weld quality is penetration depth. Automatic control of the welding process requires non-contact, non-destructive sensors that can operate in the presence of high temperatures and electrical noise found in the welding environment. Inspection using laser generation and electromagnetic acoustic transducer (EMAT) reception of ultrasound was found to satisfy these conditions. Using this technique, the time of flight of the ultrasonic wave is measured and used to calculate penetration depth. Previous works have shown that penetration depth measurement performance is drastically reduced when performed during welding. This work seeks to realize in-process penetration depth measurement by compensating for errors caused by elevated temperature. Neuro-fuzzy models are developed that predict penetration depth based on in-process time of flight measurements and the welding process input. Two scenarios are considered in which destructive penetration depth measurements are or are not available for model training. Results show the two scenarios are successful. When destructive measurements are unavailable, model error is comparable to that of offline ultrasonic measurements. When destructive measurements are available, measurement error is reduced by 50% compared to offline ultrasonic measurements. The two models can be effectively applied to permit in-process penetration depth measurements for the purpose of real-time monitoring and control. This will reduce material, production time, and labor costs and increase the quality of welded parts.en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectTime of flight diffractionen_US
dc.subjectAutomatic inspectionen_US
dc.subjectNeuro-fuzzy modelen_US
dc.subjectRayleigh waveen_US
dc.subject.lcshWelded joints Measurement
dc.subject.lcshNondestructive testing
dc.subject.lcshLasers Industrial applications
dc.subject.lcshGas metal arc welding
dc.titleIn-process sensing of weld penetration depth using non-contact laser ultrasound systemen_US
dc.typeDissertationen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.advisorCommittee Chair: Ume, Charles; Committee Member: Chen, Ye-Hwa; Committee Member: Michaels, Jennifer; Committee Member: Sadegh, Nader; Committee Member: Vachtsevanos, Georgeen_US


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