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dc.contributor.advisorSteele, John P. H.
dc.contributor.authorNeill, Andrew M.
dc.date.accessioned2017-01-19T21:59:17Z
dc.date.accessioned2022-02-03T12:56:30Z
dc.date.available2018-01-18T04:18:44Z
dc.date.available2022-02-03T12:56:30Z
dc.date.issued2016
dc.identifierT 8189
dc.identifier.urihttps://hdl.handle.net/11124/170635
dc.descriptionIncludes bibliographical references.
dc.description2016 Fall.
dc.description.abstractWelding is one of the most integral and fundamental technologies enhancing the quality and safety of our lives today. Applications range from mining to construction to automotive manufacturing, to aerospace, and most are being pushed toward higher quality standards as well as toward the adaptation of automated welding systems. Safety, cost, performance, and reliability are driving the need for faster, higher performance systems in the welding industry. There is an increasing demand for better quality monitoring and process control, especially in automated welding systems where the sensing ability and adaptive skill of an experienced human welder are no longer in the loop. Several technologies exist to monitor welding process waveforms, weld joint geometry, and provide post-process weld inspection but one of the most important goals for modern welding systems is quality monitoring and process control to achieve the desired geometry and weld quality while the weld is being made, that is, online. This would more information available to an automated welding system, enable real-time quality monitoring, and facilitate the development of adaptive closed-loop control of the welding process. Weld sensing technologies available today do not provide measurements of the three dimensional geometry during the welding process sufficient to support these capabilities. This thesis describes the development of a stereo vision system capable of providing near real-time scaled three dimensional reconstruction of a portion of the weld pool and the surface of the deposited weld bead. A pair of cameras have been mounted on a welding robot in an eye-in-hand configuration. The cameras have been calibrated to high precision and used to collect sequences of images from the welding process. These images were then rectified for stereo matching, filtered, and passed through four stereo correspondence algorithms to evaluate the algorithms for efficacy and feasibility. The results from the stereo correspondence were then used to construct a three-dimensional model of the weld bead features to a resolution of approximately 1 millimeter. The results presented in this thesis provide scaled weld pool reconstruction with a level of speed and detail that improve on the capability of current technology and establish a baseline for further development of automated welding systems. Analysis of errors, speed of calculation, and limitations of the process are included. Recommendations for future investigations based on the findings of this research are also provided.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2010-2019 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectsensing
dc.subjectwelding
dc.subjectstereo vision
dc.subjectGMAW
dc.titleApplication of 3D reconstruction by stereo vision for the purpose of assessing weld quality, The
dc.typeText
dc.contributor.committeememberMoore, Kevin L., 1960-
dc.contributor.committeememberVan Bossuyt, Douglas L.
dc.contributor.committeememberHoff, William A.
dc.contributor.committeememberPayares-Asprino, Maria C.
dcterms.embargo.terms2018-01-18
dcterms.embargo.expires2018-01-18
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorColorado School of Mines
dc.rights.accessEmbargo Expires: 01/18/2018


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