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dc.contributor.advisorChen, Heping
dc.contributor.authorHuff, Shelby A. ( )
dc.date.accessioned2018-01-10T19:16:39Z
dc.date.available2018-01-10T19:16:39Z
dc.date.issued2017-11-30
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/6930
dc.description.abstractTungsten Inert Gas (TIG) welding is the superior arc welding process used in the manufacturing industry for high quality welds. Skilled welders are capable of monitoring a weld bead and dynamically adjusting the welding parameters (current, voltage, speed, etc.) to produce a desired weld bead quality (width, height, depth). A shortage of skilled workers and motivation for industrial automation has increased research in welding process control and optimization. We propose a Machine Learning algorithm to model the TIG welding process and extract human skill. First, an automated TIG welding system uses an industrial robot to conduct aluminum welding experiments. Controlled process parameters and resultant weld bead quality measurements are used to form a welding process dataset. A Gaussian Process Regression (GPR) algorithm is applied to model the relationship in the dataset inputs variables and output variables. For a desired weld bead thickness, the required adjustment in welding current, or welding skill, can be predicted to robustly control the process. The addition of artificial intelligence to industrial robots can solve many automation solutions in the manufacturing industry dealing with complex processes.
dc.formatText
dc.format.extent65 pages
dc.format.medium1 file (.pdf)
dc.language.isoen_US
dc.subjectTIG Welding
dc.subjectArtificial Inteligence
dc.subjectMachine Learning
dc.subjectGaussian Process Regression
dc.subjectIndustrial Robots
dc.subjectManufacturing Engineering
dc.subject.lcshManufacturing processesen_US
dc.subject.lcshLight metals--Weldingen_US
dc.subject.lcshRobots--Control systemsen_US
dc.subject.lcshMachine learningen_US
dc.subject.lcshComputer algorithmsen_US
dc.titleTIG Welding Skill Extraction using a Machine Learning Algorithm
txstate.documenttypeThesis
dc.contributor.committeeMemberTate, Jitendra S.
dc.contributor.committeeMemberDroopad, Ravi
dc.contributor.committeeMemberLee, Young J.
thesis.degree.departmentEngineering
thesis.degree.disciplineManufacturing Engineering
thesis.degree.grantorTexas State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
txstate.departmentEngineering Technology


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