{"id":"https://openalex.org/W7204126140","doi":"https://doi.org/10.1007/s41939-026-01262-2","title":"Numerical analysis and neural-network modeling for predicting the thermo-viscoplastic behavior of Cu/AA2030 panel during the hot rolling process","display_name":"Numerical analysis and neural-network modeling for predicting the thermo-viscoplastic behavior of Cu/AA2030 panel during the hot rolling process","publication_year":2026,"publication_date":"2026-08-24","ids":{"openalex":"https://openalex.org/W7204126140","doi":"https://doi.org/10.1007/s41939-026-01262-2"},"language":"en","primary_location":{"id":"doi:10.1007/s41939-026-01262-2","doi":"https://doi.org/10.1007/s41939-026-01262-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41939-026-01262-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41939-026-01262-2.pdf","source":{"id":"https://openalex.org/S4210198111","display_name":"Multiscale and Multidisciplinary Modeling Experiments and Design","issn_l":"2520-8160","issn":["2520-8160","2520-8179"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","norway-1","poland-20","russia-white-list-2"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multiscale and Multidisciplinary Modeling, Experiments and Design","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s41939-026-01262-2.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122253783","display_name":"Alireza Jalili","orcid":"https://orcid.org/0000-0002-5868-9977","observed_orcids":["https://orcid.org/0000-0002-5868-9977"]},"institutions":[{"id":"https://openalex.org/I67009956","display_name":"Iran University of Science and Technology","ror":"https://ror.org/01jw2p796","country_code":"IR","type":"education","lineage":["https://openalex.org/I67009956"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Alireza Jalili","raw_affiliation_strings":["School of Mechanical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran","institution_ids":["https://openalex.org/I67009956"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104231949","display_name":"Hamid Reza Rezaei Ashtiani","orcid":"https://orcid.org/0000-0001-8500-1421","observed_orcids":["https://orcid.org/0000-0001-8500-1421"]},"institutions":[{"id":"https://openalex.org/I4210157180","display_name":"Arak University of Technology","ror":"https://ror.org/053wftt74","country_code":"IR","type":"education","lineage":["https://openalex.org/I4210157180"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Hamidreza Rezaei Ashtiani","raw_affiliation_strings":["School of Mechanical Engineering, Arak University of Technology, Arak Ave., Arak, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Arak University of Technology, Arak Ave., Arak, Iran","institution_ids":["https://openalex.org/I4210157180"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5122253783"],"corresponding_institution_ids":["https://openalex.org/I67009956"],"apc_list":{"value":3490,"currency":"USD","value_usd":3490},"apc_paid":{"value":3490,"currency":"USD","value_usd":3490},"fwci":2.929,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.87064537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"9","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11201","display_name":"Metallurgy and Material Forming","score":0.6855000257492065,"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":0.6855000257492065,"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/T10700","display_name":"Metal Forming Simulation Techniques","score":0.15440000593662262,"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"}},{"id":"https://openalex.org/T10723","display_name":"Advanced Welding Techniques Analysis","score":0.07450000196695328,"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/hot-rolling","display_name":"hot rolling","score":0.8169999718666077},{"id":"https://openalex.org/keywords/neural-network-surrogate-model","display_name":"neural network surrogate model","score":0.9430000185966492},{"id":"https://openalex.org/keywords/rolling-force-prediction","display_name":"rolling force prediction","score":0.9259999990463257},{"id":"https://openalex.org/keywords/rolling-torque","display_name":"rolling torque","score":0.8230000138282776},{"id":"https://openalex.org/keywords/friction-coefficient","display_name":"friction coefficient","score":0.7630000114440918},{"id":"https://openalex.org/keywords/reduction-ratio","display_name":"reduction ratio","score":0.6119999885559082},{"id":"https://openalex.org/keywords/rolling-speed","display_name":"rolling speed","score":0.6169999837875366},{"id":"https://openalex.org/keywords/roll-diameter","display_name":"roll diameter","score":0.7820000052452087},{"id":"https://openalex.org/keywords/interfacial-friction","display_name":"interfacial friction","score":0.7919999957084656}],"concepts":[{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6040999889373779},{"id":"https://openalex.org/C48753275","wikidata":"https://www.wikidata.org/wiki/Q11216","display_name":"Numerical analysis","level":2,"score":0.4733999967575073},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.358599990606308},{"id":"https://openalex.org/C500300565","wikidata":"https://www.wikidata.org/wiki/Q925667","display_name":"Computer simulation","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C2986605239","wikidata":"https://www.wikidata.org/wiki/Q925667","display_name":"Numerical models","level":3,"score":0.3264000117778778},{"id":"https://openalex.org/C135628077","wikidata":"https://www.wikidata.org/wiki/Q220184","display_name":"Finite element method","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.29030001163482666},{"id":"https://openalex.org/C2989031253","wikidata":"https://www.wikidata.org/wiki/Q925667","display_name":"Numerical modeling","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.2574000060558319},{"id":"https://openalex.org/C76969082","wikidata":"https://www.wikidata.org/wiki/Q486902","display_name":"Mathematical model","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s41939-026-01262-2","doi":"https://doi.org/10.1007/s41939-026-01262-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41939-026-01262-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41939-026-01262-2.pdf","source":{"id":"https://openalex.org/S4210198111","display_name":"Multiscale and Multidisciplinary Modeling Experiments and Design","issn_l":"2520-8160","issn":["2520-8160","2520-8179"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","norway-1","poland-20","russia-white-list-2"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multiscale and Multidisciplinary Modeling, Experiments and Design","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s41939-026-01262-2","doi":"https://doi.org/10.1007/s41939-026-01262-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41939-026-01262-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41939-026-01262-2.pdf","source":{"id":"https://openalex.org/S4210198111","display_name":"Multiscale and Multidisciplinary Modeling Experiments and Design","issn_l":"2520-8160","issn":["2520-8160","2520-8179"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","norway-1","poland-20","russia-white-list-2"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multiscale and Multidisciplinary Modeling, Experiments and Design","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204126140.pdf","grobid_xml":"https://content.openalex.org/works/W7204126140.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1967154341","https://openalex.org/W1969136552","https://openalex.org/W1971135427","https://openalex.org/W1983966441","https://openalex.org/W1989834844","https://openalex.org/W1991548802","https://openalex.org/W1995994366","https://openalex.org/W1998974634","https://openalex.org/W2006823312","https://openalex.org/W2019369181","https://openalex.org/W2022635608","https://openalex.org/W2043658140","https://openalex.org/W2044668963","https://openalex.org/W2045407933","https://openalex.org/W2062525109","https://openalex.org/W2094476697","https://openalex.org/W2102187992","https://openalex.org/W2116424792","https://openalex.org/W2155482699","https://openalex.org/W2769347025","https://openalex.org/W2890754941","https://openalex.org/W2971473747","https://openalex.org/W2991939752","https://openalex.org/W3049479484","https://openalex.org/W3119766571","https://openalex.org/W3151283079","https://openalex.org/W3158179159","https://openalex.org/W3186140363","https://openalex.org/W3206841340","https://openalex.org/W4302004463","https://openalex.org/W4396928136","https://openalex.org/W4404511936","https://openalex.org/W7128612588","https://openalex.org/W7140128792"],"related_works":["https://openalex.org/W7118434614","https://openalex.org/W1995994366","https://openalex.org/W4402379910","https://openalex.org/W1987764041","https://openalex.org/W2053213744","https://openalex.org/W2384589962","https://openalex.org/W2352291185","https://openalex.org/W2323502859","https://openalex.org/W3201325535","https://openalex.org/W1999545580"],"abstract_inverted_index":{"This":[0],"study":[1],"presents":[2],"a":[3,104],"three-dimensional":[4],"thermo-mechanical":[5,193],"finite":[6,85],"element":[7,86],"(3D-FEM)":[8],"investigation":[9],"of":[10,32,44,77,128,138,188,195,215],"the":[11,42,84,96,101,111,117,133,160,170,178,185,192,196,213,219],"hot":[12,91,221],"rolling":[13,63,112,139,146,154,179,222],"process":[14,33,46,209],"for":[15,29,132,145,207],"Cu/AA2030/Cu":[16],"laminated":[17],"composite":[18],"panels,":[19],"coupled":[20],"with":[21],"an":[22,203],"artificial":[23],"neural":[24],"network":[25],"(ANN)":[26],"surrogate":[27],"model":[28,37,87],"rapid":[30],"prediction":[31],"responses.":[34],"The":[35,121,152,198],"numerical":[36,97],"was":[38,107],"developed":[39,122],"to":[40,109,175],"evaluate":[41],"effects":[43],"major":[45],"parameters,":[47],"including":[48],"friction":[49,171,190],"coefficient,":[50],"reduction":[51],"ratio,":[52],"roll":[53,55],"speed,":[54],"diameter,":[56],"and":[57,68,72,79,114,130,135,150,156,165,211],"initial":[58],"copper":[59],"layer":[60],"thickness,":[61],"on":[62,95,191],"force,":[64,140],"torque,":[65],"temperature":[66],"distribution,":[67],"deformation":[69],"behavior.":[70],"Temperature-":[71],"strain":[73],"rate-dependent":[74],"material":[75],"properties":[76],"Cu":[78],"AA2030":[80],"were":[81,148,162],"incorporated":[82],"into":[83],"using":[88],"experimentally":[89],"obtained":[90,158],"compression":[92],"data.":[93],"Based":[94],"database":[98],"generated":[99],"by":[100,181],"FEM":[102],"simulations,":[103],"feed-forward":[105],"ANN":[106,123],"trained":[108],"predict":[110],"force":[113,155,180],"torque":[115,147,157],"within":[116,218],"investigated":[118,220],"operating":[119],"conditions.":[120,223],"achieved":[124],"correlation":[125],"coefficients":[126],"(R)":[127],"0.9749":[129],"0.9742":[131],"training":[134],"testing":[136],"datasets":[137],"respectively,":[141],"while":[142],"corresponding":[143],"values":[144],"0.9811":[149],"0.9810.":[151],"maximum":[153],"from":[159,173],"simulations":[161],"32.5":[163],"kN":[164],"0.445":[166],"kN\u00b7m,":[167],"respectively.":[168],"Increasing":[169],"coefficient":[172],"0.1":[174],"0.5":[176],"increased":[177],"approximately":[182],"33.8%,":[183],"demonstrating":[184],"dominant":[186],"influence":[187,214],"interfacial":[189],"response":[194],"laminate.":[197],"proposed":[199],"FEM\u2013ANN":[200],"framework":[201],"provides":[202],"efficient":[204],"computational":[205],"tool":[206],"predicting":[208],"responses":[210],"analyzing":[212],"processing":[216],"parameters":[217]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-08-25T00:00:00"}
