\"Powder Bed Fusion (PBF) is an Additive Manufacturing (AM) technique which can reduce material waste and is a fast-prototyping technique for complex geometries. However, the lack of consistent quality is a major problem in industrial applications. An InfraRed (IR) camera is a common tool to monitor a surface temperature field and melt pool morphology in PBF. With melt pool features from camera videos, manufacturing researchers use spatial feature maps to visualize defect locations and control researchers use layer-to-layer control to achieve more uniform melt pools and less defects.
Paper I presents a simulation method to characterize the effect of down-sampling on melt pool thermal feature measurements. Based on high-fidelity simulation data, the characterization reveals the complexity of the spatial down-sampling effect and provides a reference of signal quality for different thermal features under low resolutions.
Paper II presents a novel spatial, layer-to-layer, state-space control-oriented model for PBF, which transforms a temporal heat transfer model into a voxel-based spatial model, which is easy to analyze and implement. Output controllability is discussed. Simulations are presented to demonstrate the framework of spatial layer-to-layer control.
Paper III proposes a controller suitable for a layer-variant PBF system. The PBF system dynamic model is needed for control implementation but difficult to determine analytically. A MultiLayer Perceptron (MLP) network is used to classify thermal dynamics of different geometries and estimate them. With the MLP, a modified Iterative Learning Control (ILC) method is presented and analyzed, which has a better performance than a regular ILC\"-- Abstract, p. iv
","abstract_html":"<p>"Powder Bed Fusion (PBF) is an Additive Manufacturing (AM) technique which can reduce material waste and is a fast-prototyping technique for complex geometries. However, the lack of consistent quality is a major problem in industrial applications. An InfraRed (IR) camera is a common tool to monitor a surface temperature field and melt pool morphology in PBF. With melt pool features from camera videos, manufacturing researchers use spatial feature maps to visualize defect locations and control researchers use layer-to-layer control to achieve more uniform melt pools and less defects.</p> <p>Paper I presents a simulation method to characterize the effect of down-sampling on melt pool thermal feature measurements. Based on high-fidelity simulation data, the characterization reveals the complexity of the spatial down-sampling effect and provides a reference of signal quality for different thermal features under low resolutions.</p> <p>Paper II presents a novel spatial, layer-to-layer, state-space control-oriented model for PBF, which transforms a temporal heat transfer model into a voxel-based spatial model, which is easy to analyze and implement. Output controllability is discussed. Simulations are presented to demonstrate the framework of spatial layer-to-layer control.</p> <p>Paper III proposes a controller suitable for a layer-variant PBF system. The PBF system dynamic model is needed for control implementation but difficult to determine analytically. A MultiLayer Perceptron (MLP) network is used to classify thermal dynamics of different geometries and estimate them. With the MLP, a modified Iterative Learning Control (ILC) method is presented and analyzed, which has a better performance than a regular ILC"-- Abstract, p. iv</p>","abstract_has_math":false,"creators":["Wang, Xin"],"institution":"Missouri University of Science and Technology","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":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T03:18:18Z","subjects":["Control-oriented model","iterative learning control","powder bed fusion","state-space control model","Engineering","Mechanical Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/3300","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Wang, Xin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Mechanical Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Missouri University of Science and Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Control-oriented model","iterative learning control","powder bed fusion","state-space control model","Engineering","Mechanical Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/3300"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"Powder Bed Fusion (PBF) is an Additive Manufacturing (AM) technique which can reduce material waste and is a fast-prototyping technique for complex geometries. However, the lack of consistent quality is a major problem in industrial applications. An InfraRed (IR) camera is a common tool to monitor a surface temperature field and melt pool morphology in PBF. With melt pool features from camera videos, manufacturing researchers use spatial feature maps to visualize defect locations and control researchers use layer-to-layer control to achieve more uniform melt pools and less defects.
Paper I presents a simulation method to characterize the effect of down-sampling on melt pool thermal feature measurements. Based on high-fidelity simulation data, the characterization reveals the complexity of the spatial down-sampling effect and provides a reference of signal quality for different thermal features under low resolutions.
Paper II presents a novel spatial, layer-to-layer, state-space control-oriented model for PBF, which transforms a temporal heat transfer model into a voxel-based spatial model, which is easy to analyze and implement. Output controllability is discussed. Simulations are presented to demonstrate the framework of spatial layer-to-layer control.
Paper III proposes a controller suitable for a layer-variant PBF system. The PBF system dynamic model is needed for control implementation but difficult to determine analytically. A MultiLayer Perceptron (MLP) network is used to classify thermal dynamics of different geometries and estimate them. With the MLP, a modified Iterative Learning Control (ILC) method is presented and analyzed, which has a better performance than a regular ILC\"-- Abstract, p. iv
"]},{"key":"dc:title","label":"Title","values":["Melt Pool Feature Extraction, A State-Space Model and Spatial Layer-to-Layer Control for Powder Bed Fusion"]}]}],"canonical_facts":{"dc:creator":["Wang, Xin"],"dc:description.abstract":["\"Powder Bed Fusion (PBF) is an Additive Manufacturing (AM) technique which can reduce material waste and is a fast-prototyping technique for complex geometries. However, the lack of consistent quality is a major problem in industrial applications. An InfraRed (IR) camera is a common tool to monitor a surface temperature field and melt pool morphology in PBF. With melt pool features from camera videos, manufacturing researchers use spatial feature maps to visualize defect locations and control researchers use layer-to-layer control to achieve more uniform melt pools and less defects.
Paper I presents a simulation method to characterize the effect of down-sampling on melt pool thermal feature measurements. Based on high-fidelity simulation data, the characterization reveals the complexity of the spatial down-sampling effect and provides a reference of signal quality for different thermal features under low resolutions.
Paper II presents a novel spatial, layer-to-layer, state-space control-oriented model for PBF, which transforms a temporal heat transfer model into a voxel-based spatial model, which is easy to analyze and implement. Output controllability is discussed. Simulations are presented to demonstrate the framework of spatial layer-to-layer control.
Paper III proposes a controller suitable for a layer-variant PBF system. The PBF system dynamic model is needed for control implementation but difficult to determine analytically. A MultiLayer Perceptron (MLP) network is used to classify thermal dynamics of different geometries and estimate them. With the MLP, a modified Iterative Learning Control (ILC) method is presented and analyzed, which has a better performance than a regular ILC\"-- Abstract, p. iv
"],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/3300"],"dc:subject":["Control-oriented model","iterative learning control","powder bed fusion","state-space control model","Engineering","Mechanical Engineering"],"dc:title":["Melt Pool Feature Extraction, A State-Space Model and Spatial Layer-to-Layer Control for Powder Bed Fusion"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. D. in Mechanical Engineering"],"thesis:institution_name":["Missouri University of Science and Technology"]},"updated_at":"2026-07-24T03:18:18Z"}