{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79374"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79374","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Geometric Reasoning and Machine Learning: A Set of Design and Manufacturing Problems","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Jaiswal, Prakhar"],"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":2019,"date_issued":"2019-04-04T20:31:02Z","date_published":"2019-04-04T20:31:02Z","updated_at":"2026-07-27T19:05:16Z","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/10477/79374","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":["Jaiswal, Prakhar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-04-04T20:31:02Z","2019","2019-01-08 20:34:15"]},{"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/10477/79374"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Research in engineering design and manufacturing domain has enabled significant advances in the last couple of decades. Now, more than ever before, many individ-ual manual tasks are being automated to assist designers and fabricators at different stages of product lifecycle. The possibility of automating previously manually carried out tasks is being aided by the ongoing advancement of computational algorithms and ever-increasing computational power. Although the current achievement and progress in automating the design and manufacturing process are commendable, it still has a long way to go. There are a variety of design and manufacturing tasks that are still carried out manually thus leading to tedious repetitions, resources wastage, and unexplored and suboptimal solutions. In this dissertation, I research the inno-vative usage of geometric reasoning and machine learning techniques (probabilistic models and deep reinforcement learning) to create computational tools that facilitate designers and manufacturers in carrying out several distinct design and manufactur-ing tasks. Each chapter in the dissertation is targeted at solving a distinct type of problem. Hence, the solution approach for each problem is different but utilizes the common core of geometric reasoning and machine learning. Specifically, I focus on the following main categories of problems: corrective, suggestive, and generative."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Geometric Reasoning and Machine Learning: A Set of Design and Manufacturing Problems"]}]}],"canonical_facts":{"dc:contributor":["Rai, Rahul","Mechanical and Aerospace Engineering"],"dc:creator":["Jaiswal, Prakhar"],"dc:date":["2019-04-04T20:31:02Z","2019","2019-01-08 20:34:15"],"dc:description":["Ph.D.","Research in engineering design and manufacturing domain has enabled significant advances in the last couple of decades. Now, more than ever before, many individ-ual manual tasks are being automated to assist designers and fabricators at different stages of product lifecycle. The possibility of automating previously manually carried out tasks is being aided by the ongoing advancement of computational algorithms and ever-increasing computational power. Although the current achievement and progress in automating the design and manufacturing process are commendable, it still has a long way to go. There are a variety of design and manufacturing tasks that are still carried out manually thus leading to tedious repetitions, resources wastage, and unexplored and suboptimal solutions. In this dissertation, I research the inno-vative usage of geometric reasoning and machine learning techniques (probabilistic models and deep reinforcement learning) to create computational tools that facilitate designers and manufacturers in carrying out several distinct design and manufactur-ing tasks. Each chapter in the dissertation is targeted at solving a distinct type of problem. Hence, the solution approach for each problem is different but utilizes the common core of geometric reasoning and machine learning. Specifically, I focus on the following main categories of problems: corrective, suggestive, and generative."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79374"],"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":["Geometric Reasoning and Machine Learning: A Set of Design and Manufacturing Problems"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:16Z"}