{"id":"https://openalex.org/W4414110651","doi":"https://doi.org/10.1109/access.2025.3607848","title":"CLARiC: Contrastive Learning and Root-Cause Inference With Causality for Explainable Smart Manufacturing","display_name":"CLARiC: Contrastive Learning and Root-Cause Inference With Causality for Explainable Smart Manufacturing","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414110651","doi":"https://doi.org/10.1109/access.2025.3607848"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3607848","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3607848","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3607848","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054245660","display_name":"Chin\u2010Yi Lin","orcid":"https://orcid.org/0000-0002-5308-8531"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chin-Yi Lin","raw_affiliation_strings":["The University of Texas at EL Paso, El Paso, Texas, USA","University of Texas at EL Paso, El Paso, Texas, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at EL Paso, El Paso, Texas, USA","institution_ids":["https://openalex.org/I164936912"]},{"raw_affiliation_string":"University of Texas at EL Paso, El Paso, Texas, USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058784524","display_name":"Tzu-Liang Tseng","orcid":"https://orcid.org/0000-0002-3903-529X"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tzu-Liang Tseng","raw_affiliation_strings":["The University of Texas at EL Paso, El Paso, Texas, USA","University of Texas at EL Paso, El Paso, Texas, USA"],"raw_orcid":"https://orcid.org/0000-0002-3903-529X","affiliations":[{"raw_affiliation_string":"The University of Texas at EL Paso, El Paso, Texas, USA","institution_ids":["https://openalex.org/I164936912"]},{"raw_affiliation_string":"University of Texas at EL Paso, El Paso, Texas, USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007543711","display_name":"Tsung-Han Tsai","orcid":"https://orcid.org/0000-0001-9745-5957"},"institutions":[{"id":"https://openalex.org/I43566213","display_name":"National Taipei University of Business","ror":"https://ror.org/029hrv109","country_code":"TW","type":"education","lineage":["https://openalex.org/I43566213"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Tsung-Han Tsai","raw_affiliation_strings":["National Taipei University of Business, Taipei, Taiwan"],"raw_orcid":"https://orcid.org/0000-0001-9745-5957","affiliations":[{"raw_affiliation_string":"National Taipei University of Business, Taipei, Taiwan","institution_ids":["https://openalex.org/I43566213"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5054245660"],"corresponding_institution_ids":["https://openalex.org/I164936912"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":6.2328,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.96303731,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"161279","last_page":"161298"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9459999799728394,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9459999799728394,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9386000037193298,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.8999999761581421,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.6437000036239624},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.6348000168800354},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5551000237464905},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.519599974155426},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.4900999963283539},{"id":"https://openalex.org/keywords/root-cause","display_name":"Root cause","score":0.47699999809265137},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.47429999709129333},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3822999894618988},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.36160001158714294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682000160217285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6459000110626221},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.6437000036239624},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6348000168800354},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5551000237464905},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.519599974155426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5163999795913696},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C84945661","wikidata":"https://www.wikidata.org/wiki/Q7366567","display_name":"Root cause","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.47429999709129333},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3822999894618988},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.36160001158714294},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.3610000014305115},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3573000133037567},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.31540000438690186},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3607848","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3607848","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f8df38e046664880a2bac0db7ff4b742","is_oa":true,"landing_page_url":"https://doaj.org/article/f8df38e046664880a2bac0db7ff4b742","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 161279-161298 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3607848","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3607848","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3885793318","display_name":null,"funder_award_id":"P116S210004","funder_id":"https://openalex.org/F4320306106","funder_display_name":"U.S. Department of Education"},{"id":"https://openalex.org/G4195747565","display_name":null,"funder_award_id":"ERC-ASPIRE-1941524","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5065788867","display_name":null,"funder_award_id":"DUE-2216396","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G538144450","display_name":null,"funder_award_id":"P120A220044","funder_id":"https://openalex.org/F4320306106","funder_display_name":"U.S. Department of Education"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306106","display_name":"U.S. Department of Education","ror":"https://ror.org/021adze67"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1513595735","https://openalex.org/W1787224781","https://openalex.org/W1996796871","https://openalex.org/W2150291618","https://openalex.org/W2240067561","https://openalex.org/W2516809705","https://openalex.org/W2790376986","https://openalex.org/W2913085327","https://openalex.org/W2958089299","https://openalex.org/W3120783285","https://openalex.org/W3126562619","https://openalex.org/W3137061779","https://openalex.org/W3145004415","https://openalex.org/W3150893739","https://openalex.org/W3167061096","https://openalex.org/W3184853819","https://openalex.org/W3204167189","https://openalex.org/W3204740057","https://openalex.org/W3207910183","https://openalex.org/W4205512943","https://openalex.org/W4205620295","https://openalex.org/W4220992948","https://openalex.org/W4284975065","https://openalex.org/W4311628237","https://openalex.org/W4385245566","https://openalex.org/W4390246520","https://openalex.org/W4398185635","https://openalex.org/W4400409820","https://openalex.org/W4401083046","https://openalex.org/W4404659686","https://openalex.org/W4411186495"],"related_works":["https://openalex.org/W2030594396","https://openalex.org/W2535098331","https://openalex.org/W4280640835","https://openalex.org/W2885334669","https://openalex.org/W3215790726","https://openalex.org/W2202104725","https://openalex.org/W2354546531","https://openalex.org/W4295855176","https://openalex.org/W2068689476","https://openalex.org/W4242664608"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"CLARiC":[3,44,115,175,223],"(Contrastive":[4],"Learning":[5],"and":[6,21,61,111,128,167,189,202,219],"Root-cause":[7],"Inference":[8],"with":[9,152],"Causality),":[10],"a":[11,184,225],"next-generation":[12],"AI":[13,23,231],"framework":[14],"that":[15,97],"unifies":[16],"anomaly":[17,216],"detection,":[18,217],"causal":[19,86,154,169,221],"inference,":[20],"explainable":[22],"(XAI)":[24],"techniques":[25],"to":[26,37,145,149,210],"advance":[27],"the":[28,79,107,133,191],"state":[29],"of":[30],"smart":[31],"manufacturing.":[32,234],"Unlike":[33],"existing":[34],"methods":[35],"limited":[36],"correlation-based":[38],"insights":[39],"or":[40,74],"opaque":[41],"\u201cblack-box\u201d":[42],"models,":[43],"stands":[45],"out":[46],"in":[47,106,183,200,229],"four":[48],"key":[49],"ways.":[50],"First,":[51],"it":[52],"employs":[53],"multimodal":[54],"contrastive":[55],"learning\u2014fusing":[56],"sensor":[57],"streams,":[58],"text":[59],"logs,":[60],"images\u2014to":[62],"robustly":[63],"distinguish":[64],"normal":[65],"from":[66,207],"anomalous":[67],"data,":[68],"even":[69],"under":[70],"severe":[71],"class":[72],"imbalance":[73],"shifting":[75],"operational":[76],"conditions.":[77],"Second,":[78],"approach":[80],"extends":[81],"beyond":[82],"correlation":[83],"by":[84,180,195],"integrating":[85],"inference":[87],"(e.g.,":[88],"propensity":[89],"score":[90],"matching,":[91],"difference-in-differences),":[92],"thereby":[93],"identifying":[94],"high-impact":[95],"factors":[96],"genuinely":[98],"drive":[99],"process":[100],"deviations.":[101],"Third,":[102],"through":[103],"counterfactual":[104],"simulations":[105],"learned":[108],"embedding":[109],"space":[110],"controlled":[112],"real-world":[113],"interventions,":[114,222],"verifies":[116],"whether":[117],"modifying":[118],"specific":[119],"parameters":[120],"actually":[121],"enhances":[122],"manufacturing":[123],"outcomes\u2014reducing":[124],"defects,":[125],"boosting":[126],"yield,":[127],"mitigating":[129],"equipment":[130],"drifts.":[131],"Finally,":[132],"framework\u2019s":[134],"built-in":[135],"XAI":[136],"components":[137],"deliver":[138],"transparent,":[139],"fine-grained":[140],"explanations,":[141],"enabling":[142],"domain":[143],"experts":[144],"trace":[146],"anomalies":[147],"back":[148],"root":[150],"causes":[151],"clear":[153],"pathways":[155],"rather":[156],"than":[157],"post-hoc":[158],"guesswork.":[159],"Rigorous":[160],"theoretical":[161],"analyses":[162],"confirm":[163],"robust":[164],"representation":[165],"learning":[166],"consistent":[168],"predictions":[170],"across":[171],"heterogeneous":[172],"environments.":[173],"Empirically,":[174],"lowered":[176],"real-line":[177],"abnormal":[178],"rates":[179],"34":[181],"%":[182],"30-day":[185],"WBG":[186],"semiconductor":[187],"trial":[188],"outperformed":[190],"best":[192],"state-of-the-art":[193],"baseline":[194],"\u2248":[196],"3":[197],"percentage":[198],"points":[199],"accuracy":[201],"F1-score,":[203],"underscoring":[204],"its":[205],"effectiveness":[206],"wide-band-gap":[208],"semiconductors":[209],"automotive":[211],"assembly":[212],"lines.":[213],"By":[214],"bridging":[215],"interpretability,":[218],"validated":[220],"offers":[224],"decisive":[226],"step":[227],"forward":[228],"industrial":[230],"for":[232],"mission-critical":[233]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
