{"id":"https://openalex.org/W2218285471","doi":"https://doi.org/10.1109/bigdata.2015.7364081","title":"Data analytics and uncertainty quantification for energy prediction in manufacturing","display_name":"Data analytics and uncertainty quantification for energy prediction in manufacturing","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2218285471","doi":"https://doi.org/10.1109/bigdata.2015.7364081","mag":"2218285471"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2015.7364081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7364081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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/A5110359515","display_name":"Ronay Ak","orcid":"https://orcid.org/0000-0003-2768-6535"},"institutions":[{"id":"https://openalex.org/I102475099","display_name":"Sup\u00e9lec","ror":"https://ror.org/00n7gwn90","country_code":"FR","type":"education","lineage":["https://openalex.org/I102475099"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Ronay Ak","raw_affiliation_strings":["Department of Energy SUPELEC, Gif-Sur-Yvette, France"],"affiliations":[{"raw_affiliation_string":"Department of Energy SUPELEC, Gif-Sur-Yvette, France","institution_ids":["https://openalex.org/I102475099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051832961","display_name":"Raunak Bhinge","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raunak Bhinge","raw_affiliation_strings":["Mechanical Engineering University of California, Berkeley Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"Mechanical Engineering University of California, Berkeley Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5110359515"],"corresponding_institution_ids":["https://openalex.org/I102475099"],"apc_list":null,"apc_paid":null,"fwci":0.1003,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.51492736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2782","last_page":"2784"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11954","display_name":"Energy Efficiency and Management","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11954","display_name":"Energy Efficiency and Management","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11451","display_name":"Advanced Machining and Optimization Techniques","score":0.994700014591217,"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/T10188","display_name":"Advanced machining processes and optimization","score":0.9934999942779541,"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/computer-science","display_name":"Computer science","score":0.7013388872146606},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.6768996715545654},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5995412468910217},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5978835225105286},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5577728748321533},{"id":"https://openalex.org/keywords/machining","display_name":"Machining","score":0.518217146396637},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5098013281822205},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5080747604370117},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5061535239219666},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4896753132343292},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.46081751585006714},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.43790754675865173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41473960876464844},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1902465522289276}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7013388872146606},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.6768996715545654},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5995412468910217},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5978835225105286},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5577728748321533},{"id":"https://openalex.org/C523214423","wikidata":"https://www.wikidata.org/wiki/Q192047","display_name":"Machining","level":2,"score":0.518217146396637},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5098013281822205},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5080747604370117},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5061535239219666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4896753132343292},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.46081751585006714},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.43790754675865173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41473960876464844},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1902465522289276},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2015.7364081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7364081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332178","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1494137514","https://openalex.org/W1943611846","https://openalex.org/W2000304567","https://openalex.org/W2018887192","https://openalex.org/W2036718387","https://openalex.org/W2069954116","https://openalex.org/W2075609582","https://openalex.org/W2102204347","https://openalex.org/W2119866123","https://openalex.org/W2126105956","https://openalex.org/W2163559927","https://openalex.org/W2310042543","https://openalex.org/W2312543758","https://openalex.org/W2318004685","https://openalex.org/W6629518394","https://openalex.org/W6675438322"],"related_works":["https://openalex.org/W2570647323","https://openalex.org/W2206805568","https://openalex.org/W2076942471","https://openalex.org/W2863268765","https://openalex.org/W4226266853","https://openalex.org/W2896245927","https://openalex.org/W4205879366","https://openalex.org/W3027285423","https://openalex.org/W2805665112","https://openalex.org/W4238120086"],"abstract_inverted_index":{"Many":[0,55],"industries":[1],"are":[2,17,137],"applying":[3],"various":[4],"methods":[5,16,59],"for":[6,52,63,115,243],"optimizing":[7],"energy":[8,53,100,237],"use":[9],"across":[10],"the":[11,47,64,75,98,103,116,121,129,147,164,181,184,191,195,210,214,236],"manufacturing":[12,104,122],"life":[13],"cycle.":[14],"These":[15],"either":[18],"physics-based":[19],"or":[20],"data-driven.":[21],"Manufacturing":[22],"systems":[23,39],"generate":[24],"a":[25,91,106,109,231,240,245],"vast":[26],"amount":[27],"of":[28,49,56,77,105,140,157,183,194,213,239],"data":[29,37,41,69,223],"from":[30,190],"operations":[31,249],"and":[32,40,70,89,125,163,176,203,225,250],"in":[33,36,68,79,127],"simulations.":[34],"Advances":[35],"collection":[38,224],"analytics":[42,51],"(DA)":[43],"tools":[44],"have":[45],"enabled":[46],"development":[48],"predictive":[50,80],"prediction.":[54],"these":[57],"prediction":[58,130,135,205,232],"do":[60],"not":[61],"account":[62],"uncertainty":[65,78,117],"quantification-UQ":[66],"(both":[67],"model).":[71],"This":[72,82],"work":[73,83],"addresses":[74],"issue":[76],"analytics.":[81],"focuses":[84],"on":[85,108],"metal":[86],"cutting":[87],"processes":[88,123],"presents":[90],"Neural":[92],"Networks":[93],"(NNs)":[94,160],"model":[95,113,156,233],"to":[96,145,149,170,179,209,234],"predict":[97,235],"required":[99],"consumption":[101,238],"during":[102],"part":[107,246],"milling":[110],"machine.":[111],"The":[112],"accounts":[114],"associated":[118],"with":[119,221,247],"both":[120],"parameters,":[124],"assumptions":[126],"building":[128],"model.":[131],"To":[132],"achieve":[133],"this,":[134],"intervals":[136],"estimated":[138],"instead":[139],"point":[141],"predictions.":[142],"In":[143],"order":[144],"increase":[146,180],"ability":[148],"generalize":[150],"over":[151],"new":[152],"datasets,":[153],"an":[154],"ensemble":[155],"neural":[158],"networks":[159],"is":[161,168,218],"used,":[162],"k-nearest-neighbors":[165],"(k-nn)":[166],"approach":[167],"applied":[169],"identify":[171],"similar":[172],"patterns":[173,193],"between":[174],"training":[175,196],"test":[177],"datasets":[178],"accuracy":[182],"results":[185,200],"by":[186],"using":[187],"local":[188],"information":[189],"closest":[192],"sets.":[197],"Case":[198],"study":[199],"demonstrate":[201],"consistency":[202],"high":[204],"precision":[206],"as":[207],"compared":[208],"individual":[211],"NNs":[212],"ensembles.":[215],"Moreover,":[216],"it":[217],"shown":[219],"that":[220],"advanced":[222],"processing":[226],"techniques,":[227],"one":[228],"can":[229],"construct":[230],"machine":[241],"tool":[242],"machining":[244],"multiple":[248],"process":[251],"parameters.":[252]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}