Steel Phase Kinetics Modeling using Symbolic Regression
We describe an approach for empirical modeling of steel phase kinetics based on symbolic regression and genetic programming. The algorithm takes processed data gathered from dilatometer measurements and produces a system of differential equations that models the phase kinetics. Our initial results d...
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| creator | Piringer, David Bloder, Bernhard Kronberger, Gabriel |
| description | We describe an approach for empirical modeling of steel phase kinetics based
on symbolic regression and genetic programming. The algorithm takes processed
data gathered from dilatometer measurements and produces a system of
differential equations that models the phase kinetics. Our initial results
demonstrate that the proposed approach allows to identify compact differential
equations that fit the data. The model predicts ferrite, pearlite and bainite
formation for a single steel type. Martensite is not yet included in the model.
Future work shall incorporate martensite and generalize to multiple steel types
with different chemical compositions. |
| doi_str_mv | 10.48550/arxiv.2212.10284 |
| format | Article |
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on symbolic regression and genetic programming. The algorithm takes processed
data gathered from dilatometer measurements and produces a system of
differential equations that models the phase kinetics. Our initial results
demonstrate that the proposed approach allows to identify compact differential
equations that fit the data. The model predicts ferrite, pearlite and bainite
formation for a single steel type. Martensite is not yet included in the model.
Future work shall incorporate martensite and generalize to multiple steel types
with different chemical compositions.</description><identifier>DOI: 10.48550/arxiv.2212.10284</identifier><language>eng</language><subject>Computer Science - Learning ; Computer Science - Neural and Evolutionary Computing</subject><creationdate>2022-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2212.10284$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2212.10284$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Piringer, David</creatorcontrib><creatorcontrib>Bloder, Bernhard</creatorcontrib><creatorcontrib>Kronberger, Gabriel</creatorcontrib><title>Steel Phase Kinetics Modeling using Symbolic Regression</title><description>We describe an approach for empirical modeling of steel phase kinetics based
on symbolic regression and genetic programming. The algorithm takes processed
data gathered from dilatometer measurements and produces a system of
differential equations that models the phase kinetics. Our initial results
demonstrate that the proposed approach allows to identify compact differential
equations that fit the data. The model predicts ferrite, pearlite and bainite
formation for a single steel type. Martensite is not yet included in the model.
Future work shall incorporate martensite and generalize to multiple steel types
with different chemical compositions.</description><subject>Computer Science - Learning</subject><subject>Computer Science - Neural and Evolutionary Computing</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj71uwjAURr10QJQH6FS_QIL_7YwVaguCqlVhj27sa2opJCgGBG-PoCzn246-Q8gLZ6VyWrMpDOd0KoXgouRMODUidn1AbOnPH2Sky9ThIflMv_qAbeq29JhvXF92Td8mT39xO2DOqe-eyVOENuPksWOy-XjfzObF6vtzMXtbFWCsKoLzkVXeIGfgTZDKmGgqppWomG1cABEBKiEa4yVqpTA0wLVFJ5V1NoIck9d_7f15vR_SDoZLfSuo7wXyCj7TQIY</recordid><startdate>20221219</startdate><enddate>20221219</enddate><creator>Piringer, David</creator><creator>Bloder, Bernhard</creator><creator>Kronberger, Gabriel</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20221219</creationdate><title>Steel Phase Kinetics Modeling using Symbolic Regression</title><author>Piringer, David ; Bloder, Bernhard ; Kronberger, Gabriel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-d8cf09c6e10ac6d3466f690542907b8da2faa922b6c3e544edba157e834787fa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Learning</topic><topic>Computer Science - Neural and Evolutionary Computing</topic><toplevel>online_resources</toplevel><creatorcontrib>Piringer, David</creatorcontrib><creatorcontrib>Bloder, Bernhard</creatorcontrib><creatorcontrib>Kronberger, Gabriel</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Piringer, David</au><au>Bloder, Bernhard</au><au>Kronberger, Gabriel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Steel Phase Kinetics Modeling using Symbolic Regression</atitle><date>2022-12-19</date><risdate>2022</risdate><abstract>We describe an approach for empirical modeling of steel phase kinetics based
on symbolic regression and genetic programming. The algorithm takes processed
data gathered from dilatometer measurements and produces a system of
differential equations that models the phase kinetics. Our initial results
demonstrate that the proposed approach allows to identify compact differential
equations that fit the data. The model predicts ferrite, pearlite and bainite
formation for a single steel type. Martensite is not yet included in the model.
Future work shall incorporate martensite and generalize to multiple steel types
with different chemical compositions.</abstract><doi>10.48550/arxiv.2212.10284</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning Computer Science - Neural and Evolutionary Computing |
| title | Steel Phase Kinetics Modeling using Symbolic Regression |
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