{"id":"https://openalex.org/W4415744557","doi":"https://doi.org/10.23919/spa65537.2025.11215126","title":"Predictive Modeling of Machine Tool Data Using Artificial Intelligence Techniques in the Industry 4.0 Era","display_name":"Predictive Modeling of Machine Tool Data Using Artificial Intelligence Techniques in the Industry 4.0 Era","publication_year":2025,"publication_date":"2025-09-17","ids":{"openalex":"https://openalex.org/W4415744557","doi":"https://doi.org/10.23919/spa65537.2025.11215126"},"language":null,"primary_location":{"id":"doi:10.23919/spa65537.2025.11215126","is_oa":false,"landing_page_url":"https://doi.org/10.23919/spa65537.2025.11215126","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","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/A5010427529","display_name":"Ezgi K\u00fc\u00e7\u00fckba\u015f","orcid":null},"institutions":[{"id":"https://openalex.org/I12387023","display_name":"Trakya University","ror":"https://ror.org/00xa0xn82","country_code":"TR","type":"education","lineage":["https://openalex.org/I12387023"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ezgi K\u00fc\u00e7\u00fckba\u015f","raw_affiliation_strings":["Trakya University,Department of Computational Sciences,Edirne,T&#x00FC;rkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Trakya University,Department of Computational Sciences,Edirne,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I12387023"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026214071","display_name":"\u0130lke Kurt","orcid":"https://orcid.org/0000-0001-5911-9282"},"institutions":[{"id":"https://openalex.org/I12387023","display_name":"Trakya University","ror":"https://ror.org/00xa0xn82","country_code":"TR","type":"education","lineage":["https://openalex.org/I12387023"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"\u0130lke Kurt","raw_affiliation_strings":["Trakya University,Department of Biomedical Device Technology,Edirne,T&#x00FC;rkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Trakya University,Department of Biomedical Device Technology,Edirne,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I12387023"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023369251","display_name":"Sezer Ulukaya","orcid":"https://orcid.org/0000-0003-0473-7547"},"institutions":[{"id":"https://openalex.org/I12387023","display_name":"Trakya University","ror":"https://ror.org/00xa0xn82","country_code":"TR","type":"education","lineage":["https://openalex.org/I12387023"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Sezer Ulukaya","raw_affiliation_strings":["Trakya University,Department of Electrical and Electronics Engineering,Edirne,T&#x00FC;rkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Trakya University,Department of Electrical and Electronics Engineering,Edirne,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I12387023"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I12387023"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"171","last_page":"175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13324","display_name":"Scientific and Engineering Research Topics","score":0.07569999992847443,"subfield":{"id":"https://openalex.org/subfields/3506","display_name":"Periodontics"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T13324","display_name":"Scientific and Engineering Research Topics","score":0.07569999992847443,"subfield":{"id":"https://openalex.org/subfields/3506","display_name":"Periodontics"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10763","display_name":"Digital Transformation in Industry","score":0.07270000129938126,"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/T13038","display_name":"Internet of Things and AI","score":0.05869999900460243,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7886000275611877},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6636999845504761},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5329999923706055},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5234000086784363},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4772000014781952},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.40639999508857727},{"id":"https://openalex.org/keywords/machine-tool","display_name":"Machine tool","score":0.4011000096797943},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39890000224113464}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7886000275611877},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6636999845504761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6589999794960022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6050999760627747},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.583899974822998},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5329999923706055},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4772000014781952},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.40639999508857727},{"id":"https://openalex.org/C5941749","wikidata":"https://www.wikidata.org/wiki/Q19768","display_name":"Machine tool","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40070000290870667},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.3555999994277954},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3174999952316284},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2897000014781952},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.2669999897480011}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/spa65537.2025.11215126","is_oa":false,"landing_page_url":"https://doi.org/10.23919/spa65537.2025.11215126","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1583700199","https://openalex.org/W1700449338","https://openalex.org/W2982145560","https://openalex.org/W2982277720","https://openalex.org/W3091632020","https://openalex.org/W3125120841","https://openalex.org/W3136896197","https://openalex.org/W3206913269","https://openalex.org/W4210469193","https://openalex.org/W4220925901","https://openalex.org/W4224213224","https://openalex.org/W4290711230","https://openalex.org/W4316135871","https://openalex.org/W4382008945","https://openalex.org/W4384035819","https://openalex.org/W4410055945"],"related_works":[],"abstract_inverted_index":{"Thanks":[0],"to":[1,24,35,77,175],"the":[2,8,69,90,97,115,121,124,130,133,139,142,148,154,170,176,189],"advancement":[3],"in":[4,21,89,102],"computing":[5],"power":[6],"and":[7,29,58,163,196],"processing":[9],"of":[10,48,52,71,99,123,135,138,192],"sensor":[11],"data":[12,38,53,87,98,126,144],"collected":[13,39,88],"from":[14,40,75,96,129],"machines,":[15],"great":[16],"efforts":[17],"have":[18],"been":[19],"made":[20,63,95,159],"recent":[22],"years":[23],"predict":[25],"machine":[26,131],"tool":[27,183],"breakage":[28],"wear":[30,185],"that":[31],"may":[32],"cause":[33],"production":[34,103,122,179],"stop.":[36],"Long-term":[37],"sensors":[41],"constitutes":[42],"big":[43,86],"data.":[44],"A":[45],"certain":[46],"part":[47],"this":[49,67,112],"large":[50],"amount":[51],"is":[54,114,127,186],"labeled":[55],"by":[56,181],"experts":[57],"successful":[59],"predictions":[60],"can":[61,93],"be":[62,94],"for":[64,111,118],"future.":[65],"In":[66],"study,":[68],"number":[70],"features":[72],"was":[73,158],"reduced":[74],"134":[76],"10":[78],"with":[79,132,188],"three":[80,149],"different":[81],"feature":[82,194],"selection":[83,195],"methods":[84],"using":[85,104,160],"industry.":[91],"Inferences":[92],"cutting":[100],"tools":[101,116],"artificial":[105],"intelligence":[106],"algorithms.":[107,165],"The":[108,166],"main":[109],"motivation":[110],"study":[113],"used":[117],"machining.":[119],"During":[120],"tools,":[125],"received":[128],"help":[134],"sensors.":[136],"Estimation":[137],"locations":[140],"where":[141],"location":[143],"will":[145],"go":[146],"on":[147],"axes,":[150],"which":[151],"are":[152],"among":[153],"selected":[155],"discriminative":[156,193],"features,":[157],"LSTM,":[161],"RNN":[162],"ARIMA":[164],"LSTM":[167],"algorithm":[168],"has":[169],"lowest":[171],"error":[172],"rate.":[173],"According":[174],"results,":[177],"preventing":[178],"disruption":[180],"predicting":[182],"tip":[184],"possible":[187],"hybrid":[190],"approach":[191],"deep":[197],"learning-based":[198],"prediction.":[199]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-31T00:00:00"}
