{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/78600"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/78600","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Deep Learning Based Stress Prediction for Bottom-Up Stereo-Lithography (SLA) 3D Printing Process","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Khadilkar, Aditya"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Rai, Rahul","Mechanical and Aerospace Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-10-26T02:56:23Z","date_published":"2018-10-26T02:56:23Z","updated_at":"2026-07-27T19:05:12Z","subjects":["design"],"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/10477/78600","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rai, Rahul","Mechanical and Aerospace Engineering"]},{"key":"dc:creator","label":"Author","values":["Khadilkar, Aditya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-10-26T02:56:23Z","2018","2018-08-09 17:44:58"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["design"]}]},{"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/10477/78600"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","Additive Manufacturing (AM) is a fabrication process which creates a 3D part in a layer-by-layer fashion. AM allows fabrication of complex geometric parts that are difficult to fabricate using a traditional subtractive manufacturing process. Stereo-lithography (SLA) printing is an AM technique which prints the 3D part from liquid resin based on the principle of photo-polymerization. Part deformation and failure during the separation process are the key bottlenecks in printing high-quality parts using bottom-up SLA printing. Cohesive Zone Models have been successfully used to model the separation process in bottom-up SLA printing process. However, the Finite Element (FE) simulation of the separation process is prohibitively computationally expensive and thus cannot be used for online monitoring of the SLA printing process. This thesis presents a Deep Learning (DL) based framework to predict the stress distribution on the cured layer of the printed part in real time. The framework consists of (1) a new 3D model database that captures a variety of geometric features that can be found in real 3D parts and (2) FE simulation on the 3D models present in the database that is used to create inputs and corresponding labels (outputs) to train the DL network. Two different types of DL networks were trained network to predict the stress using the test dataset. Comparison between two different DL networks shows the validity of the proposed framework for prediction of stress in complex 3D parts. Results further show that computational time is drastically reduced by this framework in comparison with FE simulations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Deep Learning Based Stress Prediction for Bottom-Up Stereo-Lithography (SLA) 3D Printing Process"]}]}],"canonical_facts":{"dc:contributor":["Rai, Rahul","Mechanical and Aerospace Engineering"],"dc:creator":["Khadilkar, Aditya"],"dc:date":["2018-10-26T02:56:23Z","2018","2018-08-09 17:44:58"],"dc:description":["M.S.","Additive Manufacturing (AM) is a fabrication process which creates a 3D part in a layer-by-layer fashion. AM allows fabrication of complex geometric parts that are difficult to fabricate using a traditional subtractive manufacturing process. Stereo-lithography (SLA) printing is an AM technique which prints the 3D part from liquid resin based on the principle of photo-polymerization. Part deformation and failure during the separation process are the key bottlenecks in printing high-quality parts using bottom-up SLA printing. Cohesive Zone Models have been successfully used to model the separation process in bottom-up SLA printing process. However, the Finite Element (FE) simulation of the separation process is prohibitively computationally expensive and thus cannot be used for online monitoring of the SLA printing process. This thesis presents a Deep Learning (DL) based framework to predict the stress distribution on the cured layer of the printed part in real time. The framework consists of (1) a new 3D model database that captures a variety of geometric features that can be found in real 3D parts and (2) FE simulation on the 3D models present in the database that is used to create inputs and corresponding labels (outputs) to train the DL network. Two different types of DL networks were trained network to predict the stress using the test dataset. Comparison between two different DL networks shows the validity of the proposed framework for prediction of stress in complex 3D parts. Results further show that computational time is drastically reduced by this framework in comparison with FE simulations."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/78600"],"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":["design"],"dc:title":["Deep Learning Based Stress Prediction for Bottom-Up Stereo-Lithography (SLA) 3D Printing Process"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:12Z"}