{"id":"https://openalex.org/W4396905017","doi":"https://doi.org/10.1007/s10845-024-02409-z","title":"A novel method based on deep learning algorithms for material deformation rate detection","display_name":"A novel method based on deep learning algorithms for material deformation rate detection","publication_year":2024,"publication_date":"2024-05-14","ids":{"openalex":"https://openalex.org/W4396905017","doi":"https://doi.org/10.1007/s10845-024-02409-z"},"language":"en","primary_location":{"id":"doi:10.1007/s10845-024-02409-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10845-024-02409-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10845-024-02409-z.pdf","source":{"id":"https://openalex.org/S161464388","display_name":"Journal of Intelligent Manufacturing","issn_l":"0956-5515","issn":["0956-5515","1572-8145"],"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":"Journal of Intelligent Manufacturing","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/s10845-024-02409-z.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092125681","display_name":"Selim \u00d6zdem","orcid":"https://orcid.org/0000-0002-5633-9543"},"institutions":[{"id":"https://openalex.org/I2799360972","display_name":"Hitit \u00dcniversitesi","ror":"https://ror.org/01x8m3269","country_code":"TR","type":"education","lineage":["https://openalex.org/I2799360972"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Selim \u00d6zdem","raw_affiliation_strings":["Alaca Avni \u00c7elik Vocational School, Hitit University, 19030, \u00c7orum, Turkey"],"affiliations":[{"raw_affiliation_string":"Alaca Avni \u00c7elik Vocational School, Hitit University, 19030, \u00c7orum, Turkey","institution_ids":["https://openalex.org/I2799360972"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026951044","display_name":"\u0130lhami Muharrem Orak","orcid":"https://orcid.org/0000-0002-7219-4209"},"institutions":[{"id":"https://openalex.org/I173761726","display_name":"Karab\u00fck University","ror":"https://ror.org/04wy7gp54","country_code":"TR","type":"education","lineage":["https://openalex.org/I173761726"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"\u0130lhami Muharrem Orak","raw_affiliation_strings":["Computer Engineering Department, Faculty of Engineering, Karab\u00fck University, 78050, Karab\u00fck, Turkey"],"affiliations":[{"raw_affiliation_string":"Computer Engineering Department, Faculty of Engineering, Karab\u00fck University, 78050, Karab\u00fck, Turkey","institution_ids":["https://openalex.org/I173761726"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5092125681"],"corresponding_institution_ids":["https://openalex.org/I2799360972"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":3.3275,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.92203326,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"36","issue":"5","first_page":"3249","last_page":"3270"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9990000128746033,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9990000128746033,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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":0.5262671709060669},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5110651254653931},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4954432249069214},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4773262143135071},{"id":"https://openalex.org/keywords/deformation","display_name":"Deformation (meteorology)","score":0.4468301236629486},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.1804114580154419},{"id":"https://openalex.org/keywords/composite-material","display_name":"Composite material","score":0.07151824235916138}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5262671709060669},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5110651254653931},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4954432249069214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4773262143135071},{"id":"https://openalex.org/C204366326","wikidata":"https://www.wikidata.org/wiki/Q3027650","display_name":"Deformation (meteorology)","level":2,"score":0.4468301236629486},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.1804114580154419},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.07151824235916138}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10845-024-02409-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10845-024-02409-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10845-024-02409-z.pdf","source":{"id":"https://openalex.org/S161464388","display_name":"Journal of Intelligent Manufacturing","issn_l":"0956-5515","issn":["0956-5515","1572-8145"],"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":"Journal of Intelligent Manufacturing","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02409-z","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s10845-024-02409-z","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s10845-024-02409-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10845-024-02409-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10845-024-02409-z.pdf","source":{"id":"https://openalex.org/S161464388","display_name":"Journal of Intelligent Manufacturing","issn_l":"0956-5515","issn":["0956-5515","1572-8145"],"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":"Journal of Intelligent Manufacturing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320331000","display_name":"Hitit \u00dcniversitesi","ror":"https://ror.org/01x8m3269"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4396905017.pdf"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W140769071","https://openalex.org/W2027019914","https://openalex.org/W2046226483","https://openalex.org/W2054292522","https://openalex.org/W2194775991","https://openalex.org/W2396845390","https://openalex.org/W2616314293","https://openalex.org/W2669881333","https://openalex.org/W2792384468","https://openalex.org/W2894177142","https://openalex.org/W2905786168","https://openalex.org/W2910302825","https://openalex.org/W2944102485","https://openalex.org/W2964732160","https://openalex.org/W3011282394","https://openalex.org/W3017099824","https://openalex.org/W3031142281","https://openalex.org/W3091055370","https://openalex.org/W3092894460","https://openalex.org/W3111337346","https://openalex.org/W3122297873","https://openalex.org/W3126626526","https://openalex.org/W3156693589","https://openalex.org/W3163991029","https://openalex.org/W3164411016","https://openalex.org/W3184140566","https://openalex.org/W3188945072","https://openalex.org/W3190997979","https://openalex.org/W3211633652","https://openalex.org/W3212622503","https://openalex.org/W4213388619","https://openalex.org/W4220766187","https://openalex.org/W4220954488","https://openalex.org/W4221113606","https://openalex.org/W4226139731","https://openalex.org/W4232956181","https://openalex.org/W4281898245","https://openalex.org/W4283070256","https://openalex.org/W4286252812","https://openalex.org/W4292070208","https://openalex.org/W4296193528","https://openalex.org/W4296613174","https://openalex.org/W4303411317","https://openalex.org/W4309566793","https://openalex.org/W4311711029","https://openalex.org/W4312220268","https://openalex.org/W4315784642","https://openalex.org/W4318200437","https://openalex.org/W4323663757","https://openalex.org/W4362585492","https://openalex.org/W4367857432","https://openalex.org/W4385470227","https://openalex.org/W4385490913","https://openalex.org/W4385628938","https://openalex.org/W4388452265","https://openalex.org/W6888942246"],"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],"Given":[1],"the":[2,20,54,75,156,162,177],"significant":[3],"influence":[4],"of":[5,23,44,128,137,165,173],"microstructural":[6],"characteristics":[7],"on":[8],"a":[9,51,135,170,180],"material\u2019s":[10,55],"mechanical,":[11],"physical,":[12],"and":[13,64,97,113,120,143,189],"chemical":[14],"properties,":[15],"this":[16],"study":[17,168],"posits":[18],"that":[19],"deformation":[21,68,124],"rate":[22],"structural":[24],"steel":[25,58],"S235-JR":[26,45],"can":[27],"be":[28],"precisely":[29],"determined":[30],"by":[31],"analyzing":[32],"changes":[33],"in":[34,186],"its":[35],"microstructure.":[36],"Utilizing":[37],"advanced":[38],"artificial":[39],"intelligence":[40],"techniques,":[41],"microstructure":[42,82],"images":[43,83,94,122,175],"were":[46],"systematically":[47],"analyzed":[48],"to":[49,66,80,118,145,176],"establish":[50],"correlation":[52],"with":[53,159],"lifespan.":[56],"The":[57,126,150],"was":[59,95,131,153],"categorized":[60],"into":[61],"five":[62],"classes":[63],"subjected":[65],"varying":[67],"rates":[69],"through":[70,115],"laboratory":[71],"tensile":[72],"tests.":[73],"Post-deformation,":[74],"specimens":[76],"underwent":[77],"metallographic":[78],"procedures":[79],"obtain":[81],"via":[84],"an":[85],"light":[86],"optical":[87],"microscope":[88],"(LOM).":[89],"A":[90],"dataset":[91,172],"comprising":[92],"10000":[93],"introduced":[96],"validated":[98],"using":[99,134],"K-Fold":[100],"cross-validation.":[101],"This":[102,167],"research":[103,185],"utilized":[104],"deep":[105],"learning":[106,117],"(DL)":[107],"architectures":[108],"ResNet50,":[109],"ResNet101,":[110],"ResNet152,":[111],"VGG16,":[112],"VGG19":[114],"transfer":[116],"train":[119],"classify":[121],"containing":[123],"information.":[125],"effectiveness":[127],"these":[129],"models":[130],"meticulously":[132],"compared":[133,154],"suite":[136],"metrics":[138],"including":[139],"Accuracy,":[140],"F1-score,":[141],"Recall,":[142],"Precision":[144],"determine":[146],"their":[147],"classification":[148,151],"success.":[149],"accuracy":[152,164],"across":[155],"test":[157],"data,":[158],"ResNet50":[160],"achieving":[161],"highest":[163],"98.45%.":[166],"contributes":[169],"five-class":[171],"labeled":[174],"literature,":[178],"offering":[179],"new":[181],"resource":[182],"for":[183],"future":[184],"material":[187],"science":[188],"engineering.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-05T09:29:38.588285","created_date":"2025-10-10T00:00:00"}
