{"id":"https://openalex.org/W4401114157","doi":"https://doi.org/10.1109/ecai61503.2024.10607535","title":"Enhancing 3D Printing Infill Quality through Advanced Machine Learning","display_name":"Enhancing 3D Printing Infill Quality through Advanced Machine Learning","publication_year":2024,"publication_date":"2024-06-27","ids":{"openalex":"https://openalex.org/W4401114157","doi":"https://doi.org/10.1109/ecai61503.2024.10607535"},"language":"en","primary_location":{"id":"doi:10.1109/ecai61503.2024.10607535","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ecai61503.2024.10607535","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101755062","display_name":"Alexander Wang","orcid":"https://orcid.org/0000-0001-5911-2590"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexander Wang","raw_affiliation_strings":["Maclay School,Tallahassee,FL,32312"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Maclay School,Tallahassee,FL,32312","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024586803","display_name":"An-Tsun Wei","orcid":"https://orcid.org/0000-0003-3346-8577"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"An-Tsun Wei","raw_affiliation_strings":["Florida State University,College of Engineering,Tallahassee,FL,32307"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida State University,College of Engineering,Tallahassee,FL,32307","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101473591","display_name":"Hui Wang","orcid":"https://orcid.org/0000-0002-0023-3715"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Wang","raw_affiliation_strings":["Florida State University,College of Engineering,Tallahassee,FL,32307"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida State University,College of Engineering,Tallahassee,FL,32307","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009634429","display_name":"Hongmei Chi","orcid":"https://orcid.org/0000-0003-4610-6479"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongmei Chi","raw_affiliation_strings":["Florida A&#x0026;M University,Dept. of Comp. &#x0026; Info Sciences,Tallahassee,FL,32307"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida A&#x0026;M University,Dept. of Comp. &#x0026; Info Sciences,Tallahassee,FL,32307","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.8695999979972839,"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"}},"topics":[{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.8695999979972839,"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":0.8356999754905701,"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"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.7943000197410583,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/infill","display_name":"Infill","score":0.8310075998306274},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6500783562660217},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5987945795059204},{"id":"https://openalex.org/keywords/3d-printing","display_name":"3D printing","score":0.4190467596054077},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36614805459976196},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2534066140651703},{"id":"https://openalex.org/keywords/civil-engineering","display_name":"Civil engineering","score":0.15286412835121155},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.13611307740211487}],"concepts":[{"id":"https://openalex.org/C2781219549","wikidata":"https://www.wikidata.org/wiki/Q811475","display_name":"Infill","level":2,"score":0.8310075998306274},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6500783562660217},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5987945795059204},{"id":"https://openalex.org/C524769229","wikidata":"https://www.wikidata.org/wiki/Q229367","display_name":"3D printing","level":2,"score":0.4190467596054077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36614805459976196},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2534066140651703},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.15286412835121155},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.13611307740211487},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ecai61503.2024.10607535","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ecai61503.2024.10607535","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320308204","display_name":"Northrop Grumman","ror":"https://ror.org/05kewds18"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W3152917068","https://openalex.org/W4252606479","https://openalex.org/W4281982755","https://openalex.org/W4283824284","https://openalex.org/W4311425960","https://openalex.org/W4327593058","https://openalex.org/W4382811166","https://openalex.org/W4386133003","https://openalex.org/W4392699276"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2004911770","https://openalex.org/W3171824618","https://openalex.org/W2167084128","https://openalex.org/W2606853574","https://openalex.org/W127466177","https://openalex.org/W2137145432","https://openalex.org/W2287816963","https://openalex.org/W3208033704"],"abstract_inverted_index":{"As":[0],"3D":[1,26,83,105],"printing":[2,27,64,106],"technology":[3],"revolutionizes":[4],"manufacturing":[5,93],"processes,":[6],"the":[7,73,80,86,117,123,129,137],"quest":[8],"for":[9,21,88,103],"improving":[10],"print":[11,42],"quality":[12,24,101,134],"remains":[13],"paramount.":[14],"This":[15,70],"research":[16,96],"paper":[17],"explores":[18],"novel":[19],"avenues":[20],"enhancing":[22],"infill":[23,34,56,133],"in":[25,79],"by":[28],"applying":[29],"advanced":[30],"machine-learning":[31],"techniques.":[32],"Traditional":[33],"patterns":[35,57],"often":[36],"face":[37],"structural":[38],"integrity":[39],"and":[40,67,91,111,119,135],"overall":[41],"performance":[43],"challenges.":[44],"Leveraging":[45],"machine":[46],"learning":[47],"algorithms,":[48],"we":[49],"propose":[50],"a":[51],"comprehensive":[52],"approach":[53],"to":[54,99,121,127],"optimize":[55],"based":[58],"on":[59],"critical":[60],"parameters":[61],"such":[62],"as":[63],"speed,":[65],"Acceleration,":[66],"material":[68],"utilization.":[69],"study":[71],"underscores":[72],"transformative":[74],"potential":[75],"of":[76,82,139],"artificial":[77],"intelligence":[78],"realm":[81],"printing,":[84],"paving":[85],"way":[87],"more":[89],"innovative":[90],"sustainable":[92],"practices.":[94],"The":[95],"establishes":[97],"algorithms":[98],"support":[100],"control":[102],"optimizing":[104],"parameters,":[107],"predicting":[108],"mechanical":[109],"performance,":[110],"evaluating":[112],"3D-printed":[113],"products.":[114],"We":[115],"allow":[116],"end-user":[118],"practitioner":[120],"reduce":[122],"calibration":[124],"(ramp-up)":[125],"time":[126],"obtain":[128],"part":[130,140],"with":[131],"good":[132],"improve":[136],"productivity":[138],"production.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
