{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99197"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99197","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Spatial modeling for periodic surfaces in manufacturing","abstract":"High-resolution spatial data is essential for characterizing and monitoring surface quality in manufacturing. However, the measurement of high-resolution spatial data is generally expensive and time-consuming. Interpolation based on spatial models is a typical approach to cost-effectively acquire high-resolution data. However, conventional modeling methods fail to adequately model the spatial correlation induced by periodicity, and thus their interpolation precision is limited. In this paper, we propose a Bessel-based periodic variogram model, which enables kriging, a geostatistical interpolation method, to achieve accurate interpolation performance for common periodic surfaces. In addition, parameters of the proposed model provide valuable insights for the characterization and monitoring of spatial processes in manufacturing. Both simulated and real-world case studies are presented to demonstrate the effectiveness of the proposed method.","abstract_html":"High-resolution spatial data is essential for characterizing and monitoring surface quality in manufacturing. However, the measurement of high-resolution spatial data is generally expensive and time-consuming. Interpolation based on spatial models is a typical approach to cost-effectively acquire high-resolution data. However, conventional modeling methods fail to adequately model the spatial correlation induced by periodicity, and thus their interpolation precision is limited. In this paper, we propose a Bessel-based periodic variogram model, which enables kriging, a geostatistical interpolation method, to achieve accurate interpolation performance for common periodic surfaces. In addition, parameters of the proposed model provide valuable insights for the characterization and monitoring of spatial processes in manufacturing. Both simulated and real-world case studies are presented to demonstrate the effectiveness of the proposed method.","abstract_has_math":false,"creators":["Yang, Yuhang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Shao, Chenhui"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:21:07Z","date_published":"2018-03-13T15:21:07Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Spatial modeling","Variogram","Kriging","Interpolation","Periodic surface","Manufacturing","Ultrasonic metal welding"],"languages":["en"],"rights":["Copyright 2017 Yuhang Yang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99197","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shao, Chenhui"]},{"key":"dc:creator","label":"Author","values":["Yang, Yuhang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:21:07Z","2020-03-14T09:15:28Z","2017-11-14","2017-12"]},{"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":["Spatial modeling","Variogram","Kriging","Interpolation","Periodic surface","Manufacturing","Ultrasonic metal welding"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Yuhang Yang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99197"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["High-resolution spatial data is essential for characterizing and monitoring surface quality in manufacturing. However, the measurement of high-resolution spatial data is generally expensive and time-consuming. Interpolation based on spatial models is a typical approach to cost-effectively acquire high-resolution data. However, conventional modeling methods fail to adequately model the spatial correlation induced by periodicity, and thus their interpolation precision is limited. In this paper, we propose a Bessel-based periodic variogram model, which enables kriging, a geostatistical interpolation method, to achieve accurate interpolation performance for common periodic surfaces. In addition, parameters of the proposed model provide valuable insights for the characterization and monitoring of spatial processes in manufacturing. Both simulated and real-world case studies are presented to demonstrate the effectiveness of the proposed method.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Yuhang Yang, accepted the attached license on 2017-11-14 at 10:30.","The student, Yuhang Yang, submitted this Thesis for approval on 2017-11-14 at 10:43.","This Thesis was approved for publication on 2017-11-14 at 13:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11719 on 2018-03-13 at 09:55:40","Made available in DSpace on 2018-03-13T15:21:07Z (GMT). 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However, the measurement of high-resolution spatial data is generally expensive and time-consuming. Interpolation based on spatial models is a typical approach to cost-effectively acquire high-resolution data. However, conventional modeling methods fail to adequately model the spatial correlation induced by periodicity, and thus their interpolation precision is limited. In this paper, we propose a Bessel-based periodic variogram model, which enables kriging, a geostatistical interpolation method, to achieve accurate interpolation performance for common periodic surfaces. In addition, parameters of the proposed model provide valuable insights for the characterization and monitoring of spatial processes in manufacturing. Both simulated and real-world case studies are presented to demonstrate the effectiveness of the proposed method.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Yuhang Yang, accepted the attached license on 2017-11-14 at 10:30.","The student, Yuhang Yang, submitted this Thesis for approval on 2017-11-14 at 10:43.","This Thesis was approved for publication on 2017-11-14 at 13:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11719 on 2018-03-13 at 09:55:40","Made available in DSpace on 2018-03-13T15:21:07Z (GMT). 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