{"id":"https://openalex.org/W4293281805","doi":"https://doi.org/10.1109/tase.2022.3160420","title":"A Multi-Stage Approach for Knowledge-Guided Predictions With Application to Additive Manufacturing","display_name":"A Multi-Stage Approach for Knowledge-Guided Predictions With Application to Additive Manufacturing","publication_year":2022,"publication_date":"2022-03-28","ids":{"openalex":"https://openalex.org/W4293281805","doi":"https://doi.org/10.1109/tase.2022.3160420"},"language":"en","primary_location":{"id":"doi:10.1109/tase.2022.3160420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2022.3160420","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045078106","display_name":"Seokhyun Chung","orcid":"https://orcid.org/0000-0001-5176-4180"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Seokhyun Chung","raw_affiliation_strings":["Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":"https://orcid.org/0000-0001-5176-4180","affiliations":[{"raw_affiliation_string":"Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020421322","display_name":"Cheng-Hao Chou","orcid":"https://orcid.org/0000-0002-9028-0696"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cheng-Hao Chou","raw_affiliation_strings":["Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031351819","display_name":"Xiaozhu Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaozhu Fang","raw_affiliation_strings":["Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075324117","display_name":"Raed Al Kontar","orcid":"https://orcid.org/0000-0002-4546-324X"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raed Al Kontar","raw_affiliation_strings":["Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":"https://orcid.org/0000-0002-4546-324X","affiliations":[{"raw_affiliation_string":"Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071256360","display_name":"Chinedum E. Okwudire","orcid":"https://orcid.org/0000-0001-6910-8827"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chinedum Okwudire","raw_affiliation_strings":["Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":"https://orcid.org/0000-0001-6910-8827","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":0.4108,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.49254359,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"19","issue":"3","first_page":"1675","last_page":"1687"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10783","display_name":"Additive Manufacturing and 3D Printing Technologies","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6323340535163879},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6156494617462158},{"id":"https://openalex.org/keywords/stereolithography","display_name":"Stereolithography","score":0.597308337688446},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4933995306491852},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4661036431789398},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4392821788787842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4250847101211548},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4170224666595459},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34806907176971436},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3335028290748596},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1915850043296814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6323340535163879},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6156494617462158},{"id":"https://openalex.org/C2779154291","wikidata":"https://www.wikidata.org/wiki/Q1022471","display_name":"Stereolithography","level":2,"score":0.597308337688446},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4933995306491852},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4661036431789398},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4392821788787842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4250847101211548},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4170224666595459},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34806907176971436},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3335028290748596},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1915850043296814},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tase.2022.3160420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2022.3160420","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G2032917210","display_name":"CPS: Small: Mitigating Uncertainties in Computer Numerical Control (CNC) as a Cloud Service using Data-Driven Transfer Learning","funder_award_id":"1931950","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1567512734","https://openalex.org/W1800731030","https://openalex.org/W2029024674","https://openalex.org/W2078178396","https://openalex.org/W2111051539","https://openalex.org/W2114660177","https://openalex.org/W2117380102","https://openalex.org/W2142721422","https://openalex.org/W2148980845","https://openalex.org/W2154983631","https://openalex.org/W2225156818","https://openalex.org/W2398062891","https://openalex.org/W2400197280","https://openalex.org/W2462906003","https://openalex.org/W2497388551","https://openalex.org/W2623293810","https://openalex.org/W2734256217","https://openalex.org/W2740570963","https://openalex.org/W2886701826","https://openalex.org/W2946493557","https://openalex.org/W2950942124","https://openalex.org/W2954040150","https://openalex.org/W2981127045","https://openalex.org/W3022953487","https://openalex.org/W3024964988","https://openalex.org/W3030916542","https://openalex.org/W3092219465","https://openalex.org/W3100688298","https://openalex.org/W3102100346","https://openalex.org/W3104020727","https://openalex.org/W3160258528","https://openalex.org/W3164731060","https://openalex.org/W4287865629","https://openalex.org/W4287870652","https://openalex.org/W4293090411","https://openalex.org/W4294562888","https://openalex.org/W4393730990","https://openalex.org/W6631190155","https://openalex.org/W6640612898","https://openalex.org/W6640963894","https://openalex.org/W6684488266","https://openalex.org/W6684578138","https://openalex.org/W6684809622","https://openalex.org/W6718836005","https://openalex.org/W6729912231","https://openalex.org/W6772650050","https://openalex.org/W6773496015","https://openalex.org/W6773555910","https://openalex.org/W6863403171"],"related_works":["https://openalex.org/W2372267530","https://openalex.org/W2969189870","https://openalex.org/W3015855446","https://openalex.org/W2965643117","https://openalex.org/W4303857162","https://openalex.org/W2407375987","https://openalex.org/W3049691116","https://openalex.org/W2505726097","https://openalex.org/W2010643158","https://openalex.org/W2106867672"],"abstract_inverted_index":{"Inspired":[0],"by":[1,128],"sequential":[2,16,129,146,224],"additive":[3,103,140],"manufacturing":[4,104,135,141],"operations,":[5],"we":[6,188,219],"consider":[7],"prediction":[8],"tasks":[9],"arising":[10],"in":[11,85,134,153,160,229],"processes":[12,142],"that":[13,24,106,131,170,194,221],"comprise":[14],"of":[15,28,74,77,92,144,205,254,257],"sub-operations":[17],"and":[18,47,56,98,113,151,158,209,241],"propose":[19,189],"a":[20,35,45,50,78,119,166,177,190,235,258],"multi-stage":[21,191],"inference":[22,52,192],"procedure":[23,53,193],"exploits":[25],"prior":[26,203],"knowledge":[27,73,204,253],"the":[29,75,90,93,107,196,202,206,255],"operational":[30,60,207],"sequence.":[31],"Our":[32],"approach":[33,69,247],"decomposes":[34,195],"data-driven":[36,168],"model":[37,66,169,180],"into":[38,176,198,238],"several":[39,139],"easier":[40,199],"problems":[41],"each":[42],"corresponding":[43],"to":[44,54,70,88,118,123,250],"sub-operation":[46,79],"then":[48],"introduces":[49],"Bayesian":[51,215],"quantify":[55],"propagate":[57],"uncertainty":[58,211],"across":[59,212],"stages.":[61],"We":[62],"also":[63],"complement":[64],"our":[65,243,246],"with":[67],"an":[68],"incorporate":[71,251],"physical":[72,252],"output":[76,256],"which":[80],"is":[81,126,248],"often":[82,132],"more":[83],"practical":[84],"reality":[86],"relative":[87],"understanding":[89],"physics":[91],"entire":[94],"process.":[95],"Comprehensive":[96],"simulations":[97],"two":[99],"case":[100],"studies":[101],"on":[102],"show":[105],"proposed":[108],"framework":[109],"provides":[110],"well-quantified":[111],"uncertainties":[112],"superior":[114],"predictive":[115,121,179],"accuracy":[116],"compared":[117],"single-stage":[120],"approach.Note":[122],"Practitioners\u2014This":[124],"paper":[125],"motivated":[127],"operations":[130,225],"occur":[133],"processes.":[136],"For":[137],"example,":[138],"consist":[143],"multiple":[145],"steps,":[147],"e.g.,":[148],"printing,":[149,156],"washing,":[150],"curing":[152],"stereolithography,":[154],"or":[155],"debinding,":[157],"sintering":[159],"binder":[161],"jetting.":[162],"In":[163],"such":[164],"settings,":[165],"complex":[167,236],"blindly":[171],"throws":[172],"all":[173],"given":[174],"data":[175],"single":[178],"might":[181],"not":[182,227],"be":[183],"optimal.":[184],"To":[185],"this":[186],"end,":[187],"problem":[197],"sub-problem":[200],"using":[201,214],"sequence,":[208],"propagates":[210],"stages":[213],"neural":[216],"networks.":[217],"Here":[218],"note":[220],"even":[222],"if":[223],"are":[226],"existent":[228],"reality,":[230],"one":[231],"may":[232],"conceptually":[233],"decompose":[234],"system":[237],"simpler":[239],"pieces":[240],"exploit":[242],"procedure.":[244],"Also,":[245],"able":[249],"sub-operation.":[259]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
