{"id":{"repo_id":"must-thes","oai_identifier":"oai:scholarsmine.mst.edu:doctoral_dissertations-2767"},"canonical_url":"https://search.dev.ndltd.org/etd/must-thes/oai:scholarsmine.mst.edu:doctoral_dissertations-2767","repository":{"repo_id":"must-thes","name":"Missouri University of Science and Technology","base_url":"https://scholarsmine.mst.edu/do/oai/"},"display":{"title":"Modeling and control of friction stir welding","abstract":"\"Friction stir welding (FSW) is receiving increased attention as an efficient solid state joining process for [a] number of reasons, including its applicability to different materials and high joint efficiencies. This apparently simple technique is affected by a number of different factors, such as process parameters, tool design, material properties and boundary conditions. Therefore, a good understanding of issues related to modeling, control, and metallurgy will be necessary to exploit its maximum potential. The objectives of this study were twofold. First, a discrete neural network (NN) based adaptive controller was developed and implemented on a six-axis robotic FSW machine...Second, the effect of process parameters and tool design was studied utilizing various sensor measurements\"--Abstract, page iv.","abstract_html":""Friction stir welding (FSW) is receiving increased attention as an efficient solid state joining process for [a] number of reasons, including its applicability to different materials and high joint efficiencies. This apparently simple technique is affected by a number of different factors, such as process parameters, tool design, material properties and boundary conditions. Therefore, a good understanding of issues related to modeling, control, and metallurgy will be necessary to exploit its maximum potential. The objectives of this study were twofold. First, a discrete neural network (NN) based adaptive controller was developed and implemented on a six-axis robotic FSW machine...Second, the effect of process parameters and tool design was studied utilizing various sensor measurements"--Abstract, page iv.","abstract_has_math":false,"creators":["Kalya, Prabhanjana"],"institution":"University of Missouri--Rolla","degree_name":"Ph. D. in Mechanical Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-02-10T08:00:00Z","date_published":"2016-02-10T08:00:00Z","updated_at":"2026-07-24T03:20:02Z","subjects":["Mechanical Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/1765","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Kalya, Prabhanjana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-02-10T08:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation - Citation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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Therefore, a good understanding of issues related to modeling, control, and metallurgy will be necessary to exploit its maximum potential. The objectives of this study were twofold. First, a discrete neural network (NN) based adaptive controller was developed and implemented on a six-axis robotic FSW machine...Second, the effect of process parameters and tool design was studied utilizing various sensor measurements\"--Abstract, page iv."]},{"key":"dc:title","label":"Title","values":["Modeling and control of friction stir welding"]}]}],"canonical_facts":{"dc:creator":["Kalya, Prabhanjana"],"dc:date.available":["2016-02-10T08:00:00Z"],"dc:description.abstract":["\"Friction stir welding (FSW) is receiving increased attention as an efficient solid state joining process for [a] number of reasons, including its applicability to different materials and high joint efficiencies. This apparently simple technique is affected by a number of different factors, such as process parameters, tool design, material properties and boundary conditions. Therefore, a good understanding of issues related to modeling, control, and metallurgy will be necessary to exploit its maximum potential. The objectives of this study were twofold. First, a discrete neural network (NN) based adaptive controller was developed and implemented on a six-axis robotic FSW machine...Second, the effect of process parameters and tool design was studied utilizing various sensor measurements\"--Abstract, page iv."],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/1765"],"dc:subject":["Mechanical Engineering"],"dc:title":["Modeling and control of friction stir welding"],"dc:type":["Dissertation - Citation"],"thesis:degree_name":["Ph. D. in Mechanical Engineering"],"thesis:institution_name":["University of Missouri--Rolla"]},"updated_at":"2026-07-24T03:20:02Z"}