{"id":{"repo_id":"texas-state","oai_identifier":"oai:digital.library.txst.edu:10877/20434"},"canonical_url":"https://search.dev.ndltd.org/etd/texas-state/oai:digital.library.txst.edu:10877/20434","repository":{"repo_id":"texas-state","name":"Texas State University","base_url":"https://digital.library.txst.edu/server/oai/request"},"display":{"title":"Modeling Robotic Wire Arc Additive Manufacturing Process Using Machine Learning","abstract":"WAAM is a promising additive manufacturing process that makes use of current technologies and materials that are widespread and much more easily accessible compared to other metal AM processes. However, a significant challenge with current WAAM processes is the high surface roughness and variance of the height and width of the produced parts. These parts often require additional machining to achieve the desired dimensions and tolerances. By applying Machine Learning, this proposal aims to predict the surface roughness and dimensions of WAAM-produced parts by adjusting the welding parameters (voltage, current, travel speed, feed speed, amplitude, and wavelength), potentially minimizing or eliminating the need for post-process machining. These modeled parameters will then be used to generate the toolpath to fill any shape, regular or irregular. Current literature has focused on single passes of the torch, much like conventional 3D printers. This proposal plans to improve the process by using a weaving toolpath. For characterization, a Cognex DS 1300R laser scanner will generate a 3D point cloud of the welds produced by the WAAM process with a resolution in the micrometer range.","abstract_html":"WAAM is a promising additive manufacturing process that makes use of current technologies and materials that are widespread and much more easily accessible compared to other metal AM processes. However, a significant challenge with current WAAM processes is the high surface roughness and variance of the height and width of the produced parts. These parts often require additional machining to achieve the desired dimensions and tolerances. By applying Machine Learning, this proposal aims to predict the surface roughness and dimensions of WAAM-produced parts by adjusting the welding parameters (voltage, current, travel speed, feed speed, amplitude, and wavelength), potentially minimizing or eliminating the need for post-process machining. These modeled parameters will then be used to generate the toolpath to fill any shape, regular or irregular. Current literature has focused on single passes of the torch, much like conventional 3D printers. This proposal plans to improve the process by using a weaving toolpath. For characterization, a Cognex DS 1300R laser scanner will generate a 3D point cloud of the welds produced by the WAAM process with a resolution in the micrometer range.","abstract_has_math":false,"creators":["Gonzalez, Aaron"],"institution":"Texas State University","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Mechanical and Manufacturing Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Chen, Heping"],"committee_chairs":[],"committee_members":["Valles Molina, Damian","Asiabanpour, Bahram"],"year":2024,"date_issued":"2024-12","date_published":"2024-12","updated_at":"2026-07-27T21:22:41Z","subjects":["machine learning","WAAM","additive manufacturing"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10877/20434","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Chen, Heping"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Valles Molina, Damian","Asiabanpour, Bahram"]},{"key":"dc:creator","label":"Author","values":["Gonzalez, Aaron"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-02-25T14:24:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-02-25T14:24:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical and Manufacturing Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Texas State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["machine learning","WAAM","additive manufacturing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10877/20434"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["WAAM is a promising additive manufacturing process that makes use of current technologies and materials that are widespread and much more easily accessible compared to other metal AM processes. However, a significant challenge with current WAAM processes is the high surface roughness and variance of the height and width of the produced parts. These parts often require additional machining to achieve the desired dimensions and tolerances. By applying Machine Learning, this proposal aims to predict the surface roughness and dimensions of WAAM-produced parts by adjusting the welding parameters (voltage, current, travel speed, feed speed, amplitude, and wavelength), potentially minimizing or eliminating the need for post-process machining. These modeled parameters will then be used to generate the toolpath to fill any shape, regular or irregular. Current literature has focused on single passes of the torch, much like conventional 3D printers. This proposal plans to improve the process by using a weaving toolpath. For characterization, a Cognex DS 1300R laser scanner will generate a 3D point cloud of the welds produced by the WAAM process with a resolution in the micrometer range."]},{"key":"dc:format","label":"Dc Format","values":["Text"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["1 file (.pdf)"]},{"key":"dc:title","label":"Title","values":["Modeling Robotic Wire Arc Additive Manufacturing Process Using Machine Learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Chen, Heping"],"dc:contributor.committeemember":["Valles Molina, Damian","Asiabanpour, Bahram"],"dc:creator":["Gonzalez, Aaron"],"dc:date.accessioned":["2025-02-25T14:24:14Z"],"dc:date.available":["2025-02-25T14:24:14Z"],"dc:date.issued":["2024-12"],"dc:description.abstract":["WAAM is a promising additive manufacturing process that makes use of current technologies and materials that are widespread and much more easily accessible compared to other metal AM processes. However, a significant challenge with current WAAM processes is the high surface roughness and variance of the height and width of the produced parts. These parts often require additional machining to achieve the desired dimensions and tolerances. By applying Machine Learning, this proposal aims to predict the surface roughness and dimensions of WAAM-produced parts by adjusting the welding parameters (voltage, current, travel speed, feed speed, amplitude, and wavelength), potentially minimizing or eliminating the need for post-process machining. These modeled parameters will then be used to generate the toolpath to fill any shape, regular or irregular. Current literature has focused on single passes of the torch, much like conventional 3D printers. This proposal plans to improve the process by using a weaving toolpath. For characterization, a Cognex DS 1300R laser scanner will generate a 3D point cloud of the welds produced by the WAAM process with a resolution in the micrometer range."],"dc:format":["Text"],"dc:format.medium":["1 file (.pdf)"],"dc:identifier.uri":["https://hdl.handle.net/10877/20434"],"dc:language.iso":["en"],"dc:subject":["machine learning","WAAM","additive manufacturing"],"dc:title":["Modeling Robotic Wire Arc Additive Manufacturing Process Using Machine Learning"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical and Manufacturing Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Texas State University"]},"updated_at":"2026-07-27T21:22:41Z"}