{"id":"https://openalex.org/W3199731654","doi":"https://doi.org/10.1109/access.2021.3112281","title":"Regime-Switched Neural Networks: Flow Stress Modeling Strategy of 310s Stainless Steel During Hot Deformation","display_name":"Regime-Switched Neural Networks: Flow Stress Modeling Strategy of 310s Stainless Steel During Hot Deformation","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3199731654","doi":"https://doi.org/10.1109/access.2021.3112281","mag":"3199731654"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3112281","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3112281","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09536504.pdf","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":null,"license_id":null,"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://ieeexplore.ieee.org/ielx7/6287639/6514899/09536504.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077409923","display_name":"Jeongho Cho","orcid":"https://orcid.org/0000-0001-5162-1745"},"institutions":[{"id":"https://openalex.org/I24541011","display_name":"Soonchunhyang University","ror":"https://ror.org/03qjsrb10","country_code":"KR","type":"education","lineage":["https://openalex.org/I24541011"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jeongho Cho","raw_affiliation_strings":["Department of Electrical Engineering, Soonchunhyang University, Asan-si, 31538, Korea"],"raw_orcid":"https://orcid.org/0000-0001-5162-1745","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Soonchunhyang University, Asan-si, 31538, Korea","institution_ids":["https://openalex.org/I24541011"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085981730","display_name":"Shin-Hyung Song","orcid":null},"institutions":[{"id":"https://openalex.org/I24541011","display_name":"Soonchunhyang University","ror":"https://ror.org/03qjsrb10","country_code":"KR","type":"education","lineage":["https://openalex.org/I24541011"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Shin-Hyung Song","raw_affiliation_strings":["Department of Smart Automobile, Soonchunhyang University, Asan-si, 31538, Korea. (e-mail: neuro2@sch.ac.kr)","Department of Smart Automobile, Soonchunhyang University, Asan-si, 31538, Korea"],"raw_orcid":"https://orcid.org/0000-0002-3561-7938","affiliations":[{"raw_affiliation_string":"Department of Smart Automobile, Soonchunhyang University, Asan-si, 31538, Korea. (e-mail: neuro2@sch.ac.kr)","institution_ids":["https://openalex.org/I24541011"]},{"raw_affiliation_string":"Department of Smart Automobile, Soonchunhyang University, Asan-si, 31538, Korea","institution_ids":["https://openalex.org/I24541011"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24541011"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.12452207,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"9","issue":null,"first_page":"128202","last_page":"128208"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11201","display_name":"Metallurgy and Material Forming","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T11201","display_name":"Metallurgy and Material Forming","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T10736","display_name":"Hydrogen embrittlement and corrosion behaviors in metals","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/2506","display_name":"Metals and Alloys"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10840","display_name":"High Temperature Alloys and Creep","score":0.9650999903678894,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.7948652505874634},{"id":"https://openalex.org/keywords/arrhenius-equation","display_name":"Arrhenius equation","score":0.7454274892807007},{"id":"https://openalex.org/keywords/constitutive-equation","display_name":"Constitutive equation","score":0.7142062187194824},{"id":"https://openalex.org/keywords/flow-stress","display_name":"Flow stress","score":0.6669170260429382},{"id":"https://openalex.org/keywords/deformation","display_name":"Deformation (meteorology)","score":0.6093148589134216},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.5963507294654846},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4952615797519684},{"id":"https://openalex.org/keywords/stress","display_name":"Stress (linguistics)","score":0.4769762456417084},{"id":"https://openalex.org/keywords/strain-rate","display_name":"Strain rate","score":0.36688607931137085},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.3314818739891052},{"id":"https://openalex.org/keywords/mechanics","display_name":"Mechanics","score":0.3171626925468445},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.29556119441986084},{"id":"https://openalex.org/keywords/composite-material","display_name":"Composite material","score":0.21591287851333618},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20701411366462708},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17477944493293762},{"id":"https://openalex.org/keywords/finite-element-method","display_name":"Finite element method","score":0.14428627490997314},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08456602692604065},{"id":"https://openalex.org/keywords/classical-mechanics","display_name":"Classical mechanics","score":0.06123858690261841}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7948652505874634},{"id":"https://openalex.org/C86183883","wikidata":"https://www.wikidata.org/wiki/Q507505","display_name":"Arrhenius equation","level":3,"score":0.7454274892807007},{"id":"https://openalex.org/C202973686","wikidata":"https://www.wikidata.org/wiki/Q1937401","display_name":"Constitutive equation","level":3,"score":0.7142062187194824},{"id":"https://openalex.org/C162611839","wikidata":"https://www.wikidata.org/wiki/Q912214","display_name":"Flow stress","level":3,"score":0.6669170260429382},{"id":"https://openalex.org/C204366326","wikidata":"https://www.wikidata.org/wiki/Q3027650","display_name":"Deformation (meteorology)","level":2,"score":0.6093148589134216},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.5963507294654846},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4952615797519684},{"id":"https://openalex.org/C21036866","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.4769762456417084},{"id":"https://openalex.org/C149342994","wikidata":"https://www.wikidata.org/wiki/Q3181055","display_name":"Strain rate","level":2,"score":0.36688607931137085},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.3314818739891052},{"id":"https://openalex.org/C57879066","wikidata":"https://www.wikidata.org/wiki/Q41217","display_name":"Mechanics","level":1,"score":0.3171626925468445},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.29556119441986084},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.21591287851333618},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20701411366462708},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17477944493293762},{"id":"https://openalex.org/C135628077","wikidata":"https://www.wikidata.org/wiki/Q220184","display_name":"Finite element method","level":2,"score":0.14428627490997314},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08456602692604065},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.06123858690261841},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C148898269","wikidata":"https://www.wikidata.org/wiki/Q1108792","display_name":"Kinetics","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3112281","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3112281","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09536504.pdf","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d7d12cc7c80e40269813a1cd4316ddfa","is_oa":true,"landing_page_url":"https://doaj.org/article/d7d12cc7c80e40269813a1cd4316ddfa","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 9, Pp 128202-128208 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3112281","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3112281","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09536504.pdf","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":null,"license_id":null,"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/G2458299494","display_name":null,"funder_award_id":"10210031","funder_id":"https://openalex.org/F4320321301","funder_display_name":"Soonchunhyang University"},{"id":"https://openalex.org/G7741665947","display_name":null,"funder_award_id":"20181005","funder_id":"https://openalex.org/F4320321301","funder_display_name":"Soonchunhyang University"}],"funders":[{"id":"https://openalex.org/F4320321301","display_name":"Soonchunhyang University","ror":"https://ror.org/03qjsrb10"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3199731654.pdf","grobid_xml":"https://content.openalex.org/works/W3199731654.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1916288049","https://openalex.org/W2049203053","https://openalex.org/W2061217977","https://openalex.org/W2061913380","https://openalex.org/W2071952374","https://openalex.org/W2085898120","https://openalex.org/W2127371284","https://openalex.org/W2475103941","https://openalex.org/W2516483320","https://openalex.org/W2741077351","https://openalex.org/W2769912520","https://openalex.org/W2792839871","https://openalex.org/W2793621197","https://openalex.org/W2834072840","https://openalex.org/W2889169333","https://openalex.org/W2900835538","https://openalex.org/W2950147447","https://openalex.org/W3047623305","https://openalex.org/W3088291215","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2388872521","https://openalex.org/W2603562330","https://openalex.org/W2159581289","https://openalex.org/W2997770800","https://openalex.org/W2958985340","https://openalex.org/W2794222894","https://openalex.org/W2000044286","https://openalex.org/W2744433168","https://openalex.org/W2362212451","https://openalex.org/W2359240102"],"abstract_inverted_index":{"This":[0],"study":[1],"examines":[2],"the":[3,26,33,46,83,97,106,109,118,123,132,135,148,156,159],"high":[4],"temperature":[5],"deformation":[6],"and":[7,30,39,51,96,122],"constitutive":[8,49,94],"modeling":[9,80],"of":[10,28,36,62,82,108,117,134,158],"flow":[11,77],"stress":[12,42],"for":[13,76],"310s":[14],"stainless":[15],"steel.":[16],"To":[17],"this":[18],"end,":[19],"hot":[20],"tensile":[21],"experiments":[22],"were":[23],"conducted":[24],"under":[25],"temperatures":[27],"700\u00b0C":[29],"800\u00b0C":[31],"at":[32],"strain":[34],"rates":[35],"0.0002/s,":[37],"0.002/s,":[38],"0.02/s.":[40],"Flow":[41],"was":[43,86,112,126,138],"modeled":[44],"using":[45],"Arrhenius":[47],"type":[48],"equation":[50],"neural":[52,63,99,153],"network":[53,100],"approach.":[54],"Specifically,":[55],"Regime-Switched":[56],"Neural":[57],"Networks":[58],"(RSNN),":[59],"a":[60,72,89],"set":[61],"networks":[64],"with":[65,91,147],"switching,":[66],"has":[67],"been":[68],"newly":[69],"proposed":[70,110,136,160],"as":[71],"better":[73],"predictive":[74],"model":[75,85],"stress.":[78],"The":[79,102],"performance":[81,125],"RSNN":[84,111,137],"evaluated":[87],"through":[88],"comparison":[90,146],"traditional":[92],"Arrhenius-type":[93,120],"equations":[95],"single":[98,152],"model.":[101,162],"results":[103],"showed":[104],"that":[105],"accuracy":[107,133],"substantially":[113],"higher":[114],"than":[115,143],"those":[116],"existing":[119,149],"equations,":[121],"prediction":[124],"therefore":[127],"significantly":[128],"improved.":[129],"In":[130],"addition,":[131],"improved":[139],"by":[140],"approximately":[141],"more":[142],"24%":[144],"in":[145],"global":[150],"model\u2014a":[151],"network\u2014thus":[154],"confirming":[155],"superiority":[157],"switching":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
