Neural network prediction of the shunt current in resistance spot welding
An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt r...
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| Veröffentlicht in: | 中国焊接 2013-09, Vol.22 (3), p.73-78 |
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| container_title | 中国焊接 |
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| creator | 张勇 谢红霞 滕辉 白华 鄢君辉 汪帅兵 |
| description | An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm. |
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The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm.</description><identifier>ISSN: 1004-5341</identifier><language>eng</language><publisher>State Key Laboratory of Solidification Processing, Shaanxi Key Laboratory of Friction Welding Technologies, Northwestern Polytechnical University, Xi'an, 710072%Department of Electrical and Computer Engineering, Kettering University, Flint, Michigan, 48504, USA%Tosoh SMD, Grove City, Ohio, 43123, USA</publisher><subject>分流率 ; 电流补偿 ; 电阻点焊 ; 直径偏差 ; 神经网络模型 ; 神经网络预测模型 ; 补偿实验 ; 误差反传</subject><ispartof>中国焊接, 2013-09, Vol.22 (3), p.73-78</ispartof><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/85464X/85464X.jpg</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>张勇 谢红霞 滕辉 白华 鄢君辉 汪帅兵</creatorcontrib><title>Neural network prediction of the shunt current in resistance spot welding</title><title>中国焊接</title><addtitle>China Welding</addtitle><description>An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm.</description><subject>分流率</subject><subject>电流补偿</subject><subject>电阻点焊</subject><subject>直径偏差</subject><subject>神经网络模型</subject><subject>神经网络预测模型</subject><subject>补偿实验</subject><subject>误差反传</subject><issn>1004-5341</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNotjrtOxDAURFOAxLLwD6aiinQd27FdohWPlVbQQB05fiQOwQ52ogi-nkhLdYo5mpmLYocBaMkIxVfFdc4DAJEc-K44vtolqREFO68xfaIpWeP17GNA0aG5tyj3S5iRXlKyG31AyWafZxX0lk1xRqsdjQ_dTXHp1Jjt7T_3xcfT4_vhpTy9PR8PD6dS40rQ0jjFQDDhsKlbDEw5EFhwUTFJKkVr7urWCFphR2kLXBPHXSspk6Bqw6gh--L-3Luq4FTomiEuKWyLzW_XD42tABMggOlm3p1N3cfQfW8fmyn5L5V-Giqw5FxW5A9uaFQg</recordid><startdate>201309</startdate><enddate>201309</enddate><creator>张勇 谢红霞 滕辉 白华 鄢君辉 汪帅兵</creator><general>State Key Laboratory of Solidification Processing, Shaanxi Key Laboratory of Friction Welding Technologies, Northwestern Polytechnical University, Xi'an, 710072%Department of Electrical and Computer Engineering, Kettering University, Flint, Michigan, 48504, USA%Tosoh SMD, Grove City, Ohio, 43123, USA</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>201309</creationdate><title>Neural network prediction of the shunt current in resistance spot welding</title><author>张勇 谢红霞 滕辉 白华 鄢君辉 汪帅兵</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1284-dfa50858f1d6b105af08187825932a467f6bd8421f44b07c3f7fb94590a6d54d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>分流率</topic><topic>电流补偿</topic><topic>电阻点焊</topic><topic>直径偏差</topic><topic>神经网络模型</topic><topic>神经网络预测模型</topic><topic>补偿实验</topic><topic>误差反传</topic><toplevel>online_resources</toplevel><creatorcontrib>张勇 谢红霞 滕辉 白华 鄢君辉 汪帅兵</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>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>中国焊接</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>张勇 谢红霞 滕辉 白华 鄢君辉 汪帅兵</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural network prediction of the shunt current in resistance spot welding</atitle><jtitle>中国焊接</jtitle><addtitle>China Welding</addtitle><date>2013-09</date><risdate>2013</risdate><volume>22</volume><issue>3</issue><spage>73</spage><epage>78</epage><pages>73-78</pages><issn>1004-5341</issn><abstract>An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm.</abstract><pub>State Key Laboratory of Solidification Processing, Shaanxi Key Laboratory of Friction Welding Technologies, Northwestern Polytechnical University, Xi'an, 710072%Department of Electrical and Computer Engineering, Kettering University, Flint, Michigan, 48504, USA%Tosoh SMD, Grove City, Ohio, 43123, USA</pub><tpages>6</tpages></addata></record> |
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| ispartof | 中国焊接, 2013-09, Vol.22 (3), p.73-78 |
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| language | eng |
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| source | Alma/SFX Local Collection |
| subjects | 分流率 电流补偿 电阻点焊 直径偏差 神经网络模型 神经网络预测模型 补偿实验 误差反传 |
| title | Neural network prediction of the shunt current in resistance spot welding |
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