{"id":"https://openalex.org/W4417025022","doi":"https://doi.org/10.48550/arxiv.2512.03050","title":"Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling","display_name":"Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling","publication_year":2025,"publication_date":"2025-11-21","ids":{"openalex":"https://openalex.org/W4417025022","doi":"https://doi.org/10.48550/arxiv.2512.03050"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.03050","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.03050","pdf_url":"https://arxiv.org/pdf/2512.03050","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.03050","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085328799","display_name":"Peter Hedstr\u00f6m","orcid":"https://orcid.org/0000-0003-1102-4342"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hedstr\u00f6m, Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120588990","display_name":"Victor Lamelas Cubero","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cubero, Victor Lamelas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120588991","display_name":"J\u00f3n Sigurdsson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sigurdsson, J\u00f3n","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120637380","display_name":"Viktor \u00d6sterberg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u00d6sterberg, Viktor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052057723","display_name":"Satish Kolli","orcid":"https://orcid.org/0000-0003-3513-3092"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kolli, Satish","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025339211","display_name":"Joakim Odqvist","orcid":"https://orcid.org/0000-0003-3598-2465"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Odqvist, Joakim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hou, Ziyong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hou, Ziyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027262593","display_name":"Wangzhong Mu","orcid":"https://orcid.org/0000-0003-0533-6729"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mu, Wangzhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5001181373","display_name":"Viswanadh Gowtham Arigela","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arigela, Viswanadh Gowtham","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9545999765396118,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9545999765396118,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"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/T10386","display_name":"Microstructure and Mechanical Properties of Steels","score":0.017000000923871994,"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/T11126","display_name":"Metallurgical Processes and Thermodynamics","score":0.003800000064074993,"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/generalizability-theory","display_name":"Generalizability theory","score":0.5382000207901001},{"id":"https://openalex.org/keywords/continuous-cooling-transformation","display_name":"Continuous cooling transformation","score":0.44999998807907104},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.44369998574256897},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.38350000977516174},{"id":"https://openalex.org/keywords/machine-tool","display_name":"Machine tool","score":0.36390000581741333},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.3386000096797943},{"id":"https://openalex.org/keywords/diagram","display_name":"Diagram","score":0.33160001039505005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5995000004768372},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5773000121116638},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5559999942779541},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5382000207901001},{"id":"https://openalex.org/C77133410","wikidata":"https://www.wikidata.org/wiki/Q5165465","display_name":"Continuous cooling transformation","level":5,"score":0.44999998807907104},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.44369998574256897},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.38350000977516174},{"id":"https://openalex.org/C5941749","wikidata":"https://www.wikidata.org/wiki/Q19768","display_name":"Machine tool","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34850001335144043},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C186399060","wikidata":"https://www.wikidata.org/wiki/Q959962","display_name":"Diagram","level":2,"score":0.33160001039505005},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C2780841897","wikidata":"https://www.wikidata.org/wiki/Q25349015","display_name":"Digital manufacturing","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.2678000032901764},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C85906118","wikidata":"https://www.wikidata.org/wiki/Q186693","display_name":"Phase diagram","level":3,"score":0.25450000166893005},{"id":"https://openalex.org/C90086362","wikidata":"https://www.wikidata.org/wiki/Q1084485","display_name":"Cooling curve","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.03050","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.03050","pdf_url":"https://arxiv.org/pdf/2512.03050","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.2512.03050","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.03050","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2512.03050","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.03050","pdf_url":"https://arxiv.org/pdf/2512.03050","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning":[1],"(ML)":[2],"has":[3,22],"emerged":[4],"as":[5,51],"a":[6,54,81,92,105,173,194],"powerful":[7],"tool":[8],"for":[9,37,99,149,199],"accelerating":[10],"the":[11,62,71],"computational":[12,82,120],"design":[13,215],"and":[14,33,70,74,114,188,207],"production":[15],"of":[16,27,107,177],"materials.":[17],"In":[18],"materials":[19,49,214],"science,":[20],"ML":[21,44,89,190],"primarily":[23],"supported":[24],"large-scale":[25],"discovery":[26],"novel":[28],"compounds":[29],"using":[30],"first-principles":[31],"data":[32],"digital":[34,196],"twin":[35,197],"applications":[36],"optimizing":[38],"manufacturing":[39],"processes.":[40],"However,":[41],"applying":[42],"general-purpose":[43],"frameworks":[45],"to":[46,90,192],"complex":[47],"industrial":[48],"such":[50],"steel":[52],"remains":[53],"challenge.":[55],"A":[56],"key":[57],"obstacle":[58],"is":[59,110],"accurately":[60],"capturing":[61],"intricate":[63],"relationship":[64],"between":[65],"chemical":[66],"composition,":[67],"processing":[68],"parameters,":[69],"resulting":[72],"microstructure":[73],"properties.":[75],"To":[76],"address":[77],"this,":[78],"we":[79],"introduce":[80],"framework":[83,181],"that":[84],"combines":[85],"physical":[86],"insights":[87],"with":[88,126,185,203],"develop":[91],"physics-informed":[93],"continuous":[94],"cooling":[95,128],"transformation":[96],"(CCT)":[97],"model":[98],"steels.":[100],"Our":[101],"model,":[102],"trained":[103],"on":[104],"dataset":[106],"4,100":[108],"diagrams,":[109],"validated":[111],"against":[112],"literature":[113],"experimental":[115],"data.":[116],"It":[117,134],"demonstrates":[118],"high":[119],"efficiency,":[121],"generating":[122],"complete":[123],"CCT":[124],"diagrams":[125],"100":[127],"curves":[129],"in":[130],"under":[131],"5":[132],"seconds.":[133],"also":[135],"shows":[136,172],"strong":[137],"generalizability":[138],"across":[139,166],"alloy":[140],"steels,":[141],"achieving":[142],"phase":[143,153],"classification":[144],"F1":[145],"scores":[146],"above":[147],"88%":[148],"all":[150,167],"phases.":[151],"For":[152],"transition":[154],"temperature":[155],"regression,":[156],"it":[157],"attains":[158],"mean":[159],"absolute":[160],"errors":[161],"(MAE)":[162],"below":[163],"20":[164],"\u00b0C":[165],"phases":[168],"except":[169],"bainite,":[170],"which":[171],"slightly":[174],"higher":[175],"MAE":[176],"27":[178],"\u00b0C.":[179],"This":[180],"can":[182],"be":[183],"extended":[184],"additional":[186],"generic":[187],"customized":[189],"models":[191],"establish":[193],"universal":[195],"platform":[198],"heat":[200],"treatment.":[201],"Integration":[202],"complementary":[204],"simulation":[205],"tools":[206],"targeted":[208],"experiments":[209],"will":[210],"further":[211],"support":[212],"accelerated":[213],"workflows.":[216]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-12-05T00:00:00"}