{"id":"https://openalex.org/W4393872316","doi":"https://doi.org/10.1007/s10489-024-05402-4","title":"Physics-informed deep learning to quantify anomalies for real-time fault mitigation in 3D printing","display_name":"Physics-informed deep learning to quantify anomalies for real-time fault mitigation in 3D printing","publication_year":2024,"publication_date":"2024-03-01","ids":{"openalex":"https://openalex.org/W4393872316","doi":"https://doi.org/10.1007/s10489-024-05402-4"},"language":"en","primary_location":{"id":"doi:10.1007/s10489-024-05402-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-024-05402-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05402-4.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05402-4.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026101931","display_name":"Benjamin Uhrich","orcid":"https://orcid.org/0009-0000-1902-9362"},"institutions":[{"id":"https://openalex.org/I926574661","display_name":"Leipzig University","ror":"https://ror.org/03s7gtk40","country_code":"DE","type":"education","lineage":["https://openalex.org/I926574661"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Benjamin Uhrich","raw_affiliation_strings":["Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, Leipzig, Germany","Leipzig University, Leipzig, Germany"],"raw_orcid":"https://orcid.org/0009-0000-1902-9362","affiliations":[{"raw_affiliation_string":"Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, Leipzig, Germany","institution_ids":["https://openalex.org/I926574661"]},{"raw_affiliation_string":"Leipzig University, Leipzig, Germany","institution_ids":["https://openalex.org/I926574661"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104348646","display_name":"Nils Pfeifer","orcid":"https://orcid.org/0000-0002-5350-1445"},"institutions":[{"id":"https://openalex.org/I926574661","display_name":"Leipzig University","ror":"https://ror.org/03s7gtk40","country_code":"DE","type":"education","lineage":["https://openalex.org/I926574661"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nils Pfeifer","raw_affiliation_strings":["Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, Leipzig, Germany","Leipzig University, Leipzig, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, Leipzig, Germany","institution_ids":["https://openalex.org/I926574661"]},{"raw_affiliation_string":"Leipzig University, Leipzig, Germany","institution_ids":["https://openalex.org/I926574661"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034469128","display_name":"Martin Sch\u00e4fer","orcid":"https://orcid.org/0000-0003-3670-8388"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Sch\u00e4fer","raw_affiliation_strings":["Siemens AG, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens AG, Berlin, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032179432","display_name":"Oliver Theile","orcid":null},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Oliver Theile","raw_affiliation_strings":["Siemens AG, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens AG, Berlin, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075756237","display_name":"Erhard Rahm","orcid":"https://orcid.org/0000-0002-2665-1114"},"institutions":[{"id":"https://openalex.org/I926574661","display_name":"Leipzig University","ror":"https://ror.org/03s7gtk40","country_code":"DE","type":"education","lineage":["https://openalex.org/I926574661"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Erhard Rahm","raw_affiliation_strings":["Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, Leipzig, Germany","Leipzig University, Leipzig, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, Leipzig, Germany","institution_ids":["https://openalex.org/I926574661"]},{"raw_affiliation_string":"Leipzig University, Leipzig, Germany","institution_ids":["https://openalex.org/I926574661"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":3.1556,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.92007813,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"54","issue":"6","first_page":"4736","last_page":"4755"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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":0.9926999807357788,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/deep-learning","display_name":"Deep learning","score":0.586349606513977},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4365237355232239},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3731394112110138},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3629222512245178},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35662367939949036},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.272858202457428},{"id":"https://openalex.org/keywords/paleontology","display_name":"Paleontology","score":0.22039473056793213}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.586349606513977},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4365237355232239},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3731394112110138},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3629222512245178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35662367939949036},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.272858202457428},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.22039473056793213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10489-024-05402-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-024-05402-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05402-4.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10489-024-05402-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-024-05402-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05402-4.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8600000143051147,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G7300775409","display_name":null,"funder_award_id":"02K18D052","funder_id":"https://openalex.org/F4320321114","funder_display_name":"Bundesministerium f\u00fcr Bildung und Forschung"}],"funders":[{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4393872316.pdf","grobid_xml":"https://content.openalex.org/works/W4393872316.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1555627060","https://openalex.org/W1972457400","https://openalex.org/W1982639356","https://openalex.org/W2137983211","https://openalex.org/W2587300945","https://openalex.org/W2767160827","https://openalex.org/W2799973040","https://openalex.org/W2801556043","https://openalex.org/W2899283552","https://openalex.org/W2902546652","https://openalex.org/W2904619063","https://openalex.org/W2922281143","https://openalex.org/W3033526262","https://openalex.org/W3042939653","https://openalex.org/W3043532440","https://openalex.org/W3046874697","https://openalex.org/W3096600878","https://openalex.org/W3107647070","https://openalex.org/W3111550989","https://openalex.org/W3119602513","https://openalex.org/W3127846343","https://openalex.org/W3139866793","https://openalex.org/W3152667417","https://openalex.org/W3153200540","https://openalex.org/W3182644282","https://openalex.org/W3189996070","https://openalex.org/W3199436856","https://openalex.org/W3205426635","https://openalex.org/W4206186653","https://openalex.org/W4220772792","https://openalex.org/W4225319043","https://openalex.org/W4226239849","https://openalex.org/W4226480439","https://openalex.org/W4229019752","https://openalex.org/W4283077611","https://openalex.org/W4291511994","https://openalex.org/W4294132089","https://openalex.org/W4309622969","https://openalex.org/W4315782922","https://openalex.org/W4367357839","https://openalex.org/W4380632808","https://openalex.org/W4386071366"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W4323565446"],"abstract_inverted_index":{"Abstract":[0],"In":[1],"3D":[2],"printing":[3,35,194],"processes,":[4],"there":[5],"are":[6,219],"many":[7],"thermal":[8,169],"stress":[9],"related":[10],"defects":[11],"that":[12],"can":[13,53,69,143],"have":[14],"a":[15,134,139,229],"significant":[16],"negative":[17],"impact":[18],"on":[19,82,221],"the":[20,25,30,34,58,96,101,154,208,222],"shape":[21],"and":[22,50,79,106,123,149,171,187],"size":[23],"of":[24,33,103,111,125,157,168,199,212],"structure.":[26],"Such":[27],"anomalies":[28],"in":[29],"heat":[31,46,67,182,213,223],"transfer":[32,47,68,148,183,214],"process":[36],"need":[37,59,97],"to":[38,72,74,137,152,164,207],"be":[39,70],"detected":[40],"at":[41],"an":[42,145],"early":[43],"stage.":[44],"Understanding":[45],"is":[48,114,162,184,205,226],"crucial,":[49],"simulation":[51],"models":[52,85],"offer":[54],"insights":[55],"while":[56],"reducing":[57],"for":[60,66,98,117],"costly":[61],"experiments.":[62],"Traditional":[63],"numerical":[64],"solvers":[65],"complex":[71,104],"adapt":[73],"diverse":[75],"printed":[76],"part":[77],"geometries,":[78],"their":[80,87,115],"reliance":[81],"predefined":[83],"mathematical":[84],"limits":[86],"flexibility.":[88],"Our":[89],"physics-informed":[90],"deep":[91,230],"learning":[92],"(PIDL)":[93],"approach":[94,161],"eliminates":[95],"discretization,":[99],"simplifying":[100],"analysis":[102],"geometries":[105,198],"enabling":[107],"automation.":[108],"The":[109,160,202,216],"drawback":[110],"parametric":[112],"PIDL":[113,158],"scalability":[116],"high-dimensional":[118,190],"problems.":[119],"Computational":[120],"time,":[121],"energy":[122,146],"cost":[124],"training":[126,150],"prevent":[127],"real-time":[128],"analysis.":[129],"It":[130],"often":[131],"takes":[132],"only":[133],"few":[135],"seconds":[136],"print":[138],"single":[140],"layer.":[141],"We":[142],"show":[144],"efficient":[147],"strategy":[151],"reduce":[153],"computational":[155],"effort":[156],"significantly.":[159],"able":[163],"quantify":[165],"relevant":[166],"effects":[167],"stresses":[170],"mitigate":[172],"errors":[173],"during":[174],"selective":[175],"laser":[176],"melting":[177],"(SLM).":[178],"To":[179],"this":[180],"end,":[181],"modelled,":[185],"simulated":[186],"analysed":[188],"using":[189],"data":[191],"obtained":[192],"from":[193],"experiments":[195],"with":[196],"different":[197],"metal":[200],"components.":[201],"proposed":[203],"method":[204],"applied":[206],"solving":[209],"forward":[210],"problem":[211],"prediction.":[215],"governing":[217],"results":[218],"based":[220],"equation,":[224],"which":[225],"integrated":[227],"into":[228],"neural":[231],"network":[232],"(DNN).":[233]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}