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2021-01-20
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このアイテムへのリンクには次のURLをご利用ください:
https://doi.org/10.2355/isijinternational.46.346
このアイテムへのリンクには次のURLをご利用ください:http://hdl.handle.net/11094/26416
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ISIJI46_03_0346
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論文情報
タイトル
Evaluation of Viscosity of Mold Flux by Using Neural Network Computation
著者
Hanao, Masahito
Hanao, Masahito
Kawamoto, Masayuki
Kawamoto, Masayuki
Tanaka, Toshihiro
Tanaka, Toshihiro
Nakamoto, Masashi
Nakamoto, Masashi
キーワード等
viscosity
solidification temperature
slag
mold flux
neural network computation
regression estimation
抄録
A new estimation method of viscosity or solidification temperature of mold fluxes was proposed by applying the neural network computation. In this evaluation system, the viscosity and the solidification temperature of mold fluxes can be evaluated from the analytical compositions in multi-component systems of SiO_2–Al_2O_3–CaO–MgO–Na_2O–F–T.Fe–ZrO_2–TiO_2–BaO–MnO–B_2O_3–S–C without any conversion of S or F to sulphide or fluoride. It was found that the calculated results of the dependence of viscosity on temperature and composition agree with the experimental results more precisely than some conventional physical models for viscosity. Furthermore, viscosity of mold fluxes can be estimated precisely in the wide range of SiO_2 content.
公開者
日本鉄鋼協会
公開者の別表記
The Iron and Steel Institute of Japan
公開者 (ヨミ)
ニホン テッコウ キョウカイ
掲載誌名
ISIJ International
巻
46
号
3
開始ページ
346
終了ページ
351
刊行年月
2006
ISSN
09151559
NCID
AA10680712
DOI
info:doi/10.2355/isijinternational.46.346
URL
http://hdl.handle.net/11094/26416
権利情報
© 2006 ISIJ
言語
英語
カテゴリ
学術雑誌論文 Journal Article
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著者版フラグ
publisher
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学術雑誌論文
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text
DCTERMS.bibliographicCitation
ISIJ International.46(3) P.346-P.351
DC.title
Evaluation of Viscosity of Mold Flux by Using Neural Network Computation
DC.creator
Hanao, Masahito
Kawamoto, Masayuki
Tanaka, Toshihiro
Nakamoto, Masashi
DC.publisher
日本鉄鋼協会
DC.language" scheme="DCTERMS.RFC1766
英語
DCTERMS.issued" scheme="DCTERMS.W3CDTF
2006
DC.identifier
info:doi/10.2355/isijinternational.46.346
DC.identifier" scheme="DCTERMS.URI
http://hdl.handle.net/11094/26416
DC.subject
viscosity
solidification temperature
slag
mold flux
neural network computation
regression estimation
DCTERMS.abstract
A new estimation method of viscosity or solidification temperature of mold fluxes was proposed by applying the neural network computation. In this evaluation system, the viscosity and the solidification temperature of mold fluxes can be evaluated from the analytical compositions in multi-component systems of SiO_2–Al_2O_3–CaO–MgO–Na_2O–F–T.Fe–ZrO_2–TiO_2–BaO–MnO–B_2O_3–S–C without any conversion of S or F to sulphide or fluoride. It was found that the calculated results of the dependence of viscosity on temperature and composition agree with the experimental results more precisely than some conventional physical models for viscosity. Furthermore, viscosity of mold fluxes can be estimated precisely in the wide range of SiO_2 content.
DC.rights
© 2006 ISIJ
citation_title
Evaluation of Viscosity of Mold Flux by Using Neural Network Computation
citation_author
Hanao, Masahito
Kawamoto, Masayuki
Tanaka, Toshihiro
Nakamoto, Masashi
citation_publisher
日本鉄鋼協会
citation_language
英語
citation_date
2006
citation_journal_title
ISIJ International
citation_volume
46
citation_issue
3
citation_firstpage
346
citation_lastpage
351
citation_issn
09151559
citation_doi
info:doi/10.2355/isijinternational.46.346
citation_public_url
http://hdl.handle.net/11094/26416
citation_keywords
viscosity
solidification temperature
slag
mold flux
neural network computation
regression estimation