{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86689"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86689","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Data-Driven Modeling of Processes and Properties in Additive Manufacturing","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Roy, Mriganka"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wodo, Olga","Mechanical and Aerospace Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:36:30Z","date_published":"2025-02-21T21:36:30Z","updated_at":"2026-07-27T19:05:34Z","subjects":["mechanical engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/86689","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wodo, Olga","Mechanical and Aerospace Engineering"]},{"key":"dc:creator","label":"Author","values":["Roy, Mriganka"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:36:30Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["mechanical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/86689"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Additive manufacturing (AM) was conceptualized as a tool for rapid-prototyping and visualization, but it soon emerged as a competitive manufacturing process. AM's main attraction is the potential to manufacture parts of complex geometries through a simple and highly repetitive process of layer-by-layer deposition. Although the process is repetitive and fully automated, the interactions between the layers during the deposition process are tightly coupled and far from being well quantified. Computational models of the processes are critically needed to unravel these interactions. However, the current state-of-the-art physics-based models are computationally demanding and cannot be used for any realistic optimization or process control. To address the high-cost challenge, we built a number of surrogate models (SM) to predict the output of the printing process at a significantly reduced computational cost. We use the physics-based models to generate the data, to train the SM models, and to inform the feature engineering design. The surrogate models (SM) serve as a proxy to the physics-based and experimental models to significantly lower the cost of prediction while providing high accuracy. We introduced a unique geometry featurization that is one of the key insights from this work. Rather than directly using the part geometry, we use the gcode and translate it into a set of features (local distances from heat sources, e.g., heated depositions, and sinks, e.g., cooling surfaces). This set of features is used as an input for the SM. Moreover, we leveraged the analytical solution to the moving heat source model to determine the heat influence zone (HIZ). The size of HIZ allows deciding a priori what should be the cardinality of the distance sets. We showed that for fused filament fabrication, the size of HIZ is small; thus, the number of input parameters for the SM is small as well. In this thesis, we build several surrogate models by increasing the input complexity of the geometry, printing path, as well as the output dimensionality of consolidation degree and thermal time series profiles. Through a comprehensive set of tests and analysis, we demonstrate the high predictive power and low computational cost of the models. Specifically, we demonstrated the capabilities of our models to predict the thermal history (time series data) and consolidation degree (scalar) for points at the interfaces between roads, with acceptable accuracy (error below 10.0%) in real-time. With such performance, the models open the possibility of optimization as well as process planning, and in-situ monitoring for closed-loop control.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-Driven Modeling of Processes and Properties in Additive Manufacturing"]}]}],"canonical_facts":{"dc:contributor":["Wodo, Olga","Mechanical and Aerospace Engineering"],"dc:creator":["Roy, Mriganka"],"dc:date":["2025-02-21T21:36:30Z","2020"],"dc:description":["Ph.D.","Additive manufacturing (AM) was conceptualized as a tool for rapid-prototyping and visualization, but it soon emerged as a competitive manufacturing process. AM's main attraction is the potential to manufacture parts of complex geometries through a simple and highly repetitive process of layer-by-layer deposition. Although the process is repetitive and fully automated, the interactions between the layers during the deposition process are tightly coupled and far from being well quantified. Computational models of the processes are critically needed to unravel these interactions. However, the current state-of-the-art physics-based models are computationally demanding and cannot be used for any realistic optimization or process control. To address the high-cost challenge, we built a number of surrogate models (SM) to predict the output of the printing process at a significantly reduced computational cost. We use the physics-based models to generate the data, to train the SM models, and to inform the feature engineering design. The surrogate models (SM) serve as a proxy to the physics-based and experimental models to significantly lower the cost of prediction while providing high accuracy. We introduced a unique geometry featurization that is one of the key insights from this work. Rather than directly using the part geometry, we use the gcode and translate it into a set of features (local distances from heat sources, e.g., heated depositions, and sinks, e.g., cooling surfaces). This set of features is used as an input for the SM. Moreover, we leveraged the analytical solution to the moving heat source model to determine the heat influence zone (HIZ). The size of HIZ allows deciding a priori what should be the cardinality of the distance sets. We showed that for fused filament fabrication, the size of HIZ is small; thus, the number of input parameters for the SM is small as well. In this thesis, we build several surrogate models by increasing the input complexity of the geometry, printing path, as well as the output dimensionality of consolidation degree and thermal time series profiles. Through a comprehensive set of tests and analysis, we demonstrate the high predictive power and low computational cost of the models. Specifically, we demonstrated the capabilities of our models to predict the thermal history (time series data) and consolidation degree (scalar) for points at the interfaces between roads, with acceptable accuracy (error below 10.0%) in real-time. With such performance, the models open the possibility of optimization as well as process planning, and in-situ monitoring for closed-loop control.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86689"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["mechanical engineering"],"dc:title":["Data-Driven Modeling of Processes and Properties in Additive Manufacturing"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:34Z"}