{"id":"https://openalex.org/W4414561666","doi":"https://doi.org/10.1115/1.4069993","title":"Data-Driven 3D-Printed Material Data Prediction From Benchmark Specimens","display_name":"Data-Driven 3D-Printed Material Data Prediction From Benchmark Specimens","publication_year":2025,"publication_date":"2025-09-26","ids":{"openalex":"https://openalex.org/W4414561666","doi":"https://doi.org/10.1115/1.4069993"},"language":"en","primary_location":{"id":"doi:10.1115/1.4069993","is_oa":true,"landing_page_url":"https://doi.org/10.1115/1.4069993","pdf_url":"https://asmedigitalcollection.asme.org/computingengineering/article-pdf/doi/10.1115/1.4069993/7541697/jcise-25-1030.pdf","source":{"id":"https://openalex.org/S173178594","display_name":"Journal of Computing and Information Science in Engineering","issn_l":"1530-9827","issn":["1530-9827","1944-7078"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316053","host_organization_name":"ASM International","host_organization_lineage":["https://openalex.org/P4310316053"],"host_organization_lineage_names":["ASM International"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing and Information Science in Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://asmedigitalcollection.asme.org/computingengineering/article-pdf/doi/10.1115/1.4069993/7541697/jcise-25-1030.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071905813","display_name":"Junghun Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164627","display_name":"Forbes Hospital","ror":"https://ror.org/05a55tn50","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1310421338","https://openalex.org/I4210164627"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Junghun Lee","raw_affiliation_strings":["Carnegie Mellon University Department of Mechanical Engineering, , 5000 Forbes Avenue, , \u00a0","5000 Forbes Ave Pittsburgh, PA 15213 Pittsburgh, PA 15232"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University Department of Mechanical Engineering, , 5000 Forbes Avenue, , \u00a0","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"5000 Forbes Ave Pittsburgh, PA 15213 Pittsburgh, PA 15232","institution_ids":["https://openalex.org/I4210164627"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018232256","display_name":"Conrad S. Tucker","orcid":"https://orcid.org/0000-0001-5365-0240"},"institutions":[{"id":"https://openalex.org/I4210164627","display_name":"Forbes Hospital","ror":"https://ror.org/05a55tn50","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1310421338","https://openalex.org/I4210164627"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Conrad Tucker","raw_affiliation_strings":["Carnegie Mellon University Department of Mechanical Engineering, , 5000 Forbes Avenue, , \u00a0","5000 Forbes Avenue Pittsburgh, PA 15213"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University Department of Mechanical Engineering, , 5000 Forbes Avenue, , \u00a0","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"5000 Forbes Avenue Pittsburgh, PA 15213","institution_ids":["https://openalex.org/I4210164627"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071905813"],"corresponding_institution_ids":["https://openalex.org/I4210164627","https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38302583,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"25","issue":"11","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.9779000282287598,"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":0.9779000282287598,"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":0.9761999845504761,"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/T12080","display_name":"Injection Molding Process and Properties","score":0.9617999792098999,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/benchmark","display_name":"Benchmark (surveying)","score":0.8604000210762024},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4814000129699707},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.460999995470047},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4226999878883362},{"id":"https://openalex.org/keywords/datasheet","display_name":"Datasheet","score":0.39100000262260437},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3885999917984009},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.3882000148296356},{"id":"https://openalex.org/keywords/experimental-data","display_name":"Experimental data","score":0.38190001249313354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3817000091075897}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8604000210762024},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.489300012588501},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C2781384022","wikidata":"https://www.wikidata.org/wiki/Q1172383","display_name":"Datasheet","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3882000148296356},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.38190001249313354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34450000524520874},{"id":"https://openalex.org/C31555180","wikidata":"https://www.wikidata.org/wiki/Q3523867","display_name":"Material properties","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.34200000762939453},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.31220000982284546},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C182508753","wikidata":"https://www.wikidata.org/wiki/Q115605","display_name":"Tensile testing","level":3,"score":0.30959999561309814},{"id":"https://openalex.org/C112950240","wikidata":"https://www.wikidata.org/wiki/Q76005","display_name":"Ultimate tensile strength","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.2897000014781952},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C43486711","wikidata":"https://www.wikidata.org/wiki/Q192005","display_name":"Elastic modulus","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2639999985694885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2572000026702881},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25099998712539673},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1115/1.4069993","is_oa":true,"landing_page_url":"https://doi.org/10.1115/1.4069993","pdf_url":"https://asmedigitalcollection.asme.org/computingengineering/article-pdf/doi/10.1115/1.4069993/7541697/jcise-25-1030.pdf","source":{"id":"https://openalex.org/S173178594","display_name":"Journal of Computing and Information Science in Engineering","issn_l":"1530-9827","issn":["1530-9827","1944-7078"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316053","host_organization_name":"ASM International","host_organization_lineage":["https://openalex.org/P4310316053"],"host_organization_lineage_names":["ASM International"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing and Information Science in Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1115/1.4069993","is_oa":true,"landing_page_url":"https://doi.org/10.1115/1.4069993","pdf_url":"https://asmedigitalcollection.asme.org/computingengineering/article-pdf/doi/10.1115/1.4069993/7541697/jcise-25-1030.pdf","source":{"id":"https://openalex.org/S173178594","display_name":"Journal of Computing and Information Science in Engineering","issn_l":"1530-9827","issn":["1530-9827","1944-7078"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316053","host_organization_name":"ASM International","host_organization_lineage":["https://openalex.org/P4310316053"],"host_organization_lineage_names":["ASM International"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing and Information Science in Engineering","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414561666.pdf","grobid_xml":"https://content.openalex.org/works/W4414561666.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1196385606","https://openalex.org/W2065441337","https://openalex.org/W2085281262","https://openalex.org/W2143289538","https://openalex.org/W2427211147","https://openalex.org/W2604483118","https://openalex.org/W2725015551","https://openalex.org/W2911203223","https://openalex.org/W2952084398","https://openalex.org/W2963563190","https://openalex.org/W2971367363","https://openalex.org/W2998098840","https://openalex.org/W3007580811","https://openalex.org/W3010707235","https://openalex.org/W3080214406","https://openalex.org/W3083648880","https://openalex.org/W3117613324","https://openalex.org/W3118358525","https://openalex.org/W3132825741","https://openalex.org/W3150167741","https://openalex.org/W3203701644","https://openalex.org/W4205224077","https://openalex.org/W4214577611","https://openalex.org/W4220687818","https://openalex.org/W4220775061","https://openalex.org/W4225264762","https://openalex.org/W4249517230","https://openalex.org/W4281397389","https://openalex.org/W4282593078","https://openalex.org/W4295269951","https://openalex.org/W4308577029","https://openalex.org/W4311424267","https://openalex.org/W4315648001","https://openalex.org/W4385637865","https://openalex.org/W4385852333","https://openalex.org/W4386838651","https://openalex.org/W4387811851","https://openalex.org/W4395067292","https://openalex.org/W4406758017"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Predicting":[1],"the":[2,17,22,33,39,46,50,58,71,102],"mechanical":[3,23,72],"behavior":[4,24,40,73],"of":[5,25,41,49,57,74,87,201],"3D-printed":[6,26,75],"products":[7],"requires":[8],"accurate":[9],"material":[10,30,93,105],"data.":[11,106,124,163],"However,":[12],"this":[13],"is":[14],"challenging":[15],"because":[16],"printing":[18,47],"process":[19,48,99],"significantly":[20,217],"affects":[21],"parts.":[27],"Consequently,":[28],"filament":[29,92],"data":[31],"from":[32,55,77],"datasheet":[34],"may":[35],"not":[36],"accurately":[37],"represent":[38],"a":[42,66,85,192],"user\u2019s":[43],"product":[44],"if":[45],"benchmark":[51,78,95,122,172],"test":[52,80,96,123],"specimen":[53,79,173],"differs":[54],"that":[56,143,167],"user.":[59],"To":[60],"address":[61],"these":[62],"issues,":[63],"we":[64,83,126],"propose":[65],"data-driven":[67],"method":[68],"for":[69,188,195,205,212],"predicting":[70],"parts":[76],"results.":[81],"First,":[82],"generate":[84],"dataset":[86,108],"10,000":[88],"points,":[89],"each":[90],"including":[91],"data,":[94,97,174],"3D-printing":[98],"parameters,":[100],"and":[101,121,130,154,175,191,208],"corresponding":[103],"effective":[104],"This":[107],"includes":[109],"features":[110],"often":[111],"overlooked":[112],"in":[113,150],"prior":[114],"studies,":[115],"such":[116],"as":[117],"interlayer":[118,169],"bonding":[119,170],"perfection":[120],"Next,":[125],"train":[127],"diverse":[128],"single":[129],"stacked":[131],"machine":[132],"learning":[133],"models":[134,149,177],"with":[135],"different":[136],"input":[137],"features.":[138],"The":[139,181],"statistical":[140],"analysis":[141,165],"shows":[142],"multilayered":[144],"perceptron":[145],"(MLP)":[146],"outperforms":[147],"other":[148],"both":[151],"feature":[152,189],"extraction":[153,190],"downstream":[155,196],"tasks.":[156],"Finally,":[157],"predictions":[158],"are":[159],"validated":[160],"against":[161],"experimental":[162],"Statistical":[164],"confirms":[166],"incorporating":[168],"perfection,":[171],"stacking":[176],"enhances":[178],"prediction":[179],"accuracy.":[180],"best-performing":[182],"model,":[183],"which":[184],"utilizes":[185],"an":[186],"MLP":[187],"polynomial":[193],"model":[194],"prediction,":[197,216],"achieves":[198],"average":[199],"errors":[200],"0.06":[202],"GPa":[203],"(4%)":[204],"Young\u2019s":[206],"modulus":[207],"1.3":[209],"MPa":[210],"(5%)":[211],"tensile":[213],"yield":[214],"strength":[215],"outperforming":[218],"previous":[219],"methods.":[220]},"counts_by_year":[],"updated_date":"2026-03-14T06:41:57.775601","created_date":"2025-10-10T00:00:00"}
