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Forecasting of mechanical properties of covered electrode containing La/CeO2 based on fuzzy neutral network
In order to improve the mechanical properties of deposited metal of ilmenite type welding electrode, CeO2/La rare earth elements were added into electrodes based on E4301 electrode, then electrodes were produced, test plates were welded, and mechanical properties were tested based on National Standa...
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| Published in: | Rare metals 2018-02, Vol.37 (2), p.161-166 |
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| creator | Xu, Yi-Jun Guo, Yong-Huan Fan, Hui |
| description | In order to improve the mechanical properties of deposited metal of ilmenite type welding electrode, CeO2/La rare earth elements were added into electrodes based on E4301 electrode, then electrodes were produced, test plates were welded, and mechanical properties were tested based on National Standards of China. For the sake of solving the problems of large amount of mechanical properties tests, long test cycle and high test cost during the conventional production process of electrode, a prediction model of the mechanical properties of deposited metal based on Takagi-Sugeno （T-S） fuzzy neural network was established. Mn, Si and C contents of medium manganese in electrode, CeO2, and La contents of electrode and welding speed were selected as input variables of the prediction model, and the tensile strength, lower yield strength, elongation, impact energy and hardness of de- posited metal were selected as output variables. Finally, predicting experiment was done under test samples, and results show that average relative prediction error of the tensile strength, lower yield strength, elongation and hardness are 0.91%, 2.57 %, 4.94 % and 1.94 %, respec- tively, which reach the need of actual production. The re- sults of prediction show that the mechanical properties of deposited metal of electrode containing rare earth can be forecasted accurately through material composition of electrode and welding parameters based on T-S fuzzy neural network model. |
| doi_str_mv | 10.1007/s12598-015-0474-9 |
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For the sake of solving the problems of large amount of mechanical properties tests, long test cycle and high test cost during the conventional production process of electrode, a prediction model of the mechanical properties of deposited metal based on Takagi-Sugeno （T-S） fuzzy neural network was established. Mn, Si and C contents of medium manganese in electrode, CeO2, and La contents of electrode and welding speed were selected as input variables of the prediction model, and the tensile strength, lower yield strength, elongation, impact energy and hardness of de- posited metal were selected as output variables. Finally, predicting experiment was done under test samples, and results show that average relative prediction error of the tensile strength, lower yield strength, elongation and hardness are 0.91%, 2.57 %, 4.94 % and 1.94 %, respec- tively, which reach the need of actual production. The re- sults of prediction show that the mechanical properties of deposited metal of electrode containing rare earth can be forecasted accurately through material composition of electrode and welding parameters based on T-S fuzzy neural network model.</description><identifier>ISSN: 1001-0521</identifier><identifier>EISSN: 1867-7185</identifier><identifier>DOI: 10.1007/s12598-015-0474-9</identifier><language>eng</language><publisher>Beijing: Nonferrous Metals Society of China</publisher><subject>Artificial neural networks ; Biomaterials ; Cerium oxides ; Chemistry and Materials Science ; Electrodes ; Elongation ; Energy ; Fuzzy logic ; Ilmenite ; Manganese ; Materials Engineering ; Materials Science ; Mathematical models ; Mechanical properties ; Metallic Materials ; Nanoscale Science and Technology ; Neural networks ; Physical Chemistry ; Prediction models ; Rare earth elements ; Tensile strength ; Welding parameters ; Yield strength ; Yield stress ; 模糊神经网络模型;机械性质测试;焊接电极;预报;稀土元素;生产过程;测试盘;国家标准</subject><ispartof>Rare metals, 2018-02, Vol.37 (2), p.161-166</ispartof><rights>The Nonferrous Metals Society of China and Springer-Verlag Berlin Heidelberg 2015</rights><rights>Rare Metals is a copyright of Springer, (2015). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c343t-561ee7af4c594ee7141a9f3981e47a1464e918bba9f10fcfca367f0c80eb7f443</citedby><cites>FETCH-LOGICAL-c343t-561ee7af4c594ee7141a9f3981e47a1464e918bba9f10fcfca367f0c80eb7f443</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/85314X/85314X.jpg</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids></links><search><creatorcontrib>Xu, Yi-Jun</creatorcontrib><creatorcontrib>Guo, Yong-Huan</creatorcontrib><creatorcontrib>Fan, Hui</creatorcontrib><title>Forecasting of mechanical properties of covered electrode containing La/CeO2 based on fuzzy neutral network</title><title>Rare metals</title><addtitle>Rare Met</addtitle><addtitle>Rare Metals</addtitle><description>In order to improve the mechanical properties of deposited metal of ilmenite type welding electrode, CeO2/La rare earth elements were added into electrodes based on E4301 electrode, then electrodes were produced, test plates were welded, and mechanical properties were tested based on National Standards of China. For the sake of solving the problems of large amount of mechanical properties tests, long test cycle and high test cost during the conventional production process of electrode, a prediction model of the mechanical properties of deposited metal based on Takagi-Sugeno （T-S） fuzzy neural network was established. Mn, Si and C contents of medium manganese in electrode, CeO2, and La contents of electrode and welding speed were selected as input variables of the prediction model, and the tensile strength, lower yield strength, elongation, impact energy and hardness of de- posited metal were selected as output variables. Finally, predicting experiment was done under test samples, and results show that average relative prediction error of the tensile strength, lower yield strength, elongation and hardness are 0.91%, 2.57 %, 4.94 % and 1.94 %, respec- tively, which reach the need of actual production. The re- sults of prediction show that the mechanical properties of deposited metal of electrode containing rare earth can be forecasted accurately through material composition of electrode and welding parameters based on T-S fuzzy neural network model.</description><subject>Artificial neural networks</subject><subject>Biomaterials</subject><subject>Cerium oxides</subject><subject>Chemistry and Materials Science</subject><subject>Electrodes</subject><subject>Elongation</subject><subject>Energy</subject><subject>Fuzzy logic</subject><subject>Ilmenite</subject><subject>Manganese</subject><subject>Materials Engineering</subject><subject>Materials Science</subject><subject>Mathematical models</subject><subject>Mechanical properties</subject><subject>Metallic Materials</subject><subject>Nanoscale Science and Technology</subject><subject>Neural networks</subject><subject>Physical Chemistry</subject><subject>Prediction models</subject><subject>Rare earth elements</subject><subject>Tensile strength</subject><subject>Welding parameters</subject><subject>Yield strength</subject><subject>Yield stress</subject><subject>模糊神经网络模型;机械性质测试;焊接电极;预报;稀土元素;生产过程;测试盘;国家标准</subject><issn>1001-0521</issn><issn>1867-7185</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPwzAQhCMEEuXxA7hFcA7djZ04PqKKl1SpFzhbjlmXQGu3dgqCX4-jVIgTJ6_W881oJ8suEK4RQEwjlpVsCsCqAC54IQ-yCTa1KAQ21WGaAbCAqsTj7CTGNwDO6xom2fudD2R07Du3zL3N12ReteuMXuWb4DcU-o7i8GH8BwV6yWlFpg_-hdLG9bpzAzjX0xktyrzVMUm8y-3u-_srd7TrQ3Jy1H_68H6WHVm9inS-f0-z57vbp9lDMV_cP85u5oVhnPVFVSOR0JabSvI0IUctLZMNEhcaec1JYtO2aYlgjTWa1cKCaYBaYTlnp9nV6Jsu2O4o9urN74JLkQqlZAyBgUgqHFUm-BgDWbUJ3VqHL4Wghk7V2KlKnaqhUyUTU45MTFq3pPDH-R_och_06t1ym7jfpFpwUYtSMPYDowKGUw</recordid><startdate>20180201</startdate><enddate>20180201</enddate><creator>Xu, Yi-Jun</creator><creator>Guo, Yong-Huan</creator><creator>Fan, Hui</creator><general>Nonferrous Metals Society of China</general><general>Springer Nature B.V</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20180201</creationdate><title>Forecasting of mechanical properties of covered electrode containing La/CeO2 based on fuzzy neutral network</title><author>Xu, Yi-Jun ; Guo, Yong-Huan ; Fan, Hui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c343t-561ee7af4c594ee7141a9f3981e47a1464e918bba9f10fcfca367f0c80eb7f443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Artificial neural networks</topic><topic>Biomaterials</topic><topic>Cerium oxides</topic><topic>Chemistry and Materials Science</topic><topic>Electrodes</topic><topic>Elongation</topic><topic>Energy</topic><topic>Fuzzy logic</topic><topic>Ilmenite</topic><topic>Manganese</topic><topic>Materials Engineering</topic><topic>Materials Science</topic><topic>Mathematical models</topic><topic>Mechanical properties</topic><topic>Metallic Materials</topic><topic>Nanoscale Science and Technology</topic><topic>Neural networks</topic><topic>Physical Chemistry</topic><topic>Prediction models</topic><topic>Rare earth elements</topic><topic>Tensile strength</topic><topic>Welding parameters</topic><topic>Yield strength</topic><topic>Yield stress</topic><topic>模糊神经网络模型;机械性质测试;焊接电极;预报;稀土元素;生产过程;测试盘;国家标准</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Yi-Jun</creatorcontrib><creatorcontrib>Guo, Yong-Huan</creatorcontrib><creatorcontrib>Fan, Hui</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Databases</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Materials Science Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Rare metals</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Yi-Jun</au><au>Guo, Yong-Huan</au><au>Fan, Hui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Forecasting of mechanical properties of covered electrode containing La/CeO2 based on fuzzy neutral network</atitle><jtitle>Rare metals</jtitle><stitle>Rare Met</stitle><addtitle>Rare Metals</addtitle><date>2018-02-01</date><risdate>2018</risdate><volume>37</volume><issue>2</issue><spage>161</spage><epage>166</epage><pages>161-166</pages><issn>1001-0521</issn><eissn>1867-7185</eissn><notes>In order to improve the mechanical properties of deposited metal of ilmenite type welding electrode, CeO2/La rare earth elements were added into electrodes based on E4301 electrode, then electrodes were produced, test plates were welded, and mechanical properties were tested based on National Standards of China. For the sake of solving the problems of large amount of mechanical properties tests, long test cycle and high test cost during the conventional production process of electrode, a prediction model of the mechanical properties of deposited metal based on Takagi-Sugeno （T-S） fuzzy neural network was established. Mn, Si and C contents of medium manganese in electrode, CeO2, and La contents of electrode and welding speed were selected as input variables of the prediction model, and the tensile strength, lower yield strength, elongation, impact energy and hardness of de- posited metal were selected as output variables. Finally, predicting experiment was done under test samples, and results show that average relative prediction error of the tensile strength, lower yield strength, elongation and hardness are 0.91%, 2.57 %, 4.94 % and 1.94 %, respec- tively, which reach the need of actual production. The re- sults of prediction show that the mechanical properties of deposited metal of electrode containing rare earth can be forecasted accurately through material composition of electrode and welding parameters based on T-S fuzzy neural network model.</notes><notes>Covered electrode; Rare earth element; Fuzzyneural network; Mechanical properties; Prediction model</notes><notes>11-2112/TF</notes><abstract>In order to improve the mechanical properties of deposited metal of ilmenite type welding electrode, CeO2/La rare earth elements were added into electrodes based on E4301 electrode, then electrodes were produced, test plates were welded, and mechanical properties were tested based on National Standards of China. For the sake of solving the problems of large amount of mechanical properties tests, long test cycle and high test cost during the conventional production process of electrode, a prediction model of the mechanical properties of deposited metal based on Takagi-Sugeno （T-S） fuzzy neural network was established. Mn, Si and C contents of medium manganese in electrode, CeO2, and La contents of electrode and welding speed were selected as input variables of the prediction model, and the tensile strength, lower yield strength, elongation, impact energy and hardness of de- posited metal were selected as output variables. Finally, predicting experiment was done under test samples, and results show that average relative prediction error of the tensile strength, lower yield strength, elongation and hardness are 0.91%, 2.57 %, 4.94 % and 1.94 %, respec- tively, which reach the need of actual production. The re- sults of prediction show that the mechanical properties of deposited metal of electrode containing rare earth can be forecasted accurately through material composition of electrode and welding parameters based on T-S fuzzy neural network model.</abstract><cop>Beijing</cop><pub>Nonferrous Metals Society of China</pub><doi>10.1007/s12598-015-0474-9</doi><tpages>6</tpages></addata></record> |
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| subjects | Artificial neural networks Biomaterials Cerium oxides Chemistry and Materials Science Electrodes Elongation Energy Fuzzy logic Ilmenite Manganese Materials Engineering Materials Science Mathematical models Mechanical properties Metallic Materials Nanoscale Science and Technology Neural networks Physical Chemistry Prediction models Rare earth elements Tensile strength Welding parameters Yield strength Yield stress 模糊神经网络模型 机械性质测试 焊接电极 预报 稀土元素 生产过程 测试盘 国家标准 |
| title | Forecasting of mechanical properties of covered electrode containing La/CeO2 based on fuzzy neutral network |
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