{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120082"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120082","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Iterative learning control of direct write additive manufacturing using online process monitoring","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Urbanski, Christopher John"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Alleyne, Andrew"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:56Z","subjects":["Additive Manufacturing","3d Printing","Direct Write Printing","Material Extrusion","Iterative Learning Control","Process Monitoring","Process Control","3d Scanning"],"languages":["en","eng"],"rights":["Copyright 2023 Christopher Urbanski"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120082","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alleyne, Andrew"]},{"key":"dc:creator","label":"Author","values":["Urbanski, Christopher John"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-02"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Additive Manufacturing","3d Printing","Direct Write Printing","Material Extrusion","Iterative Learning Control","Process Monitoring","Process Control","3d Scanning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Christopher Urbanski"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120082"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Christopher Urbanski, accepted the attached license on 2023-04-26 at 08:33.","The student, Christopher Urbanski, submitted this Thesis for approval on 2023-04-26 at 13:49.","This Thesis was approved for publication on 2023-05-02 at 09:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18095 on 2023-09-01 at 16:55:05","The development of in situ process monitoring techniques for additive manufacturing (AM) has increased in recent years. While extrusion-based AM methods enable the fabrication of complex structures, ensuring the geometric accuracy of these structures requires direct measurements of the deposited material. Moreover, part fidelity can be improved by implementing control strategies to correct the geometric errors detected through process monitoring. Despite current research focusing on in situ process monitoring, few efforts investigate the relationships between process inputs and resulting print geometry for extrusion-based AM. Consequently, process control is often based on causal relationships or neglected altogether. This work presents a process monitoring and control strategy for reducing the geometric errors in parts fabricated via extrusion-based direct write printing. A laser scanner integrated into the AM system directly measures the deposited material in situ during the print but not in real time. These measurements are processed online with a custom algorithm to determine the material’s spatial placement and bead width errors. An iterative learning control (ILC) algorithm is applied to the deposition process to compensate for the geometric errors. We experimentally validate the process monitoring and control strategy on a direct write printing system by fabricating 3D periodic lattice structures, specifically functionally graded scaffolds. Here, the ILC algorithm uses the online measurements to learn the errors in the structure’s repetitive elements as they are printed, then corrects the errors in subsequently fabricated elements. The results show improved material bead widths in scaffolds fabricated using the proposed process monitoring and control strategy."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Iterative learning control of direct write additive manufacturing using online process monitoring"]}]}],"canonical_facts":{"dc:contributor":["Alleyne, Andrew"],"dc:creator":["Urbanski, Christopher John"],"dc:date":["2023-05","2023-05-02"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Christopher Urbanski, accepted the attached license on 2023-04-26 at 08:33.","The student, Christopher Urbanski, submitted this Thesis for approval on 2023-04-26 at 13:49.","This Thesis was approved for publication on 2023-05-02 at 09:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18095 on 2023-09-01 at 16:55:05","The development of in situ process monitoring techniques for additive manufacturing (AM) has increased in recent years. While extrusion-based AM methods enable the fabrication of complex structures, ensuring the geometric accuracy of these structures requires direct measurements of the deposited material. Moreover, part fidelity can be improved by implementing control strategies to correct the geometric errors detected through process monitoring. Despite current research focusing on in situ process monitoring, few efforts investigate the relationships between process inputs and resulting print geometry for extrusion-based AM. Consequently, process control is often based on causal relationships or neglected altogether. This work presents a process monitoring and control strategy for reducing the geometric errors in parts fabricated via extrusion-based direct write printing. A laser scanner integrated into the AM system directly measures the deposited material in situ during the print but not in real time. These measurements are processed online with a custom algorithm to determine the material’s spatial placement and bead width errors. An iterative learning control (ILC) algorithm is applied to the deposition process to compensate for the geometric errors. We experimentally validate the process monitoring and control strategy on a direct write printing system by fabricating 3D periodic lattice structures, specifically functionally graded scaffolds. Here, the ILC algorithm uses the online measurements to learn the errors in the structure’s repetitive elements as they are printed, then corrects the errors in subsequently fabricated elements. The results show improved material bead widths in scaffolds fabricated using the proposed process monitoring and control strategy."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120082"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Christopher Urbanski"],"dc:subject":["Additive Manufacturing","3d Printing","Direct Write Printing","Material Extrusion","Iterative Learning Control","Process Monitoring","Process Control","3d Scanning"],"dc:title":["Iterative learning control of direct write additive manufacturing using online process monitoring"],"dc:type":["text"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}