周琼宇,谢蔚,王小芬,王操,胡义锋.基于人工神经网络预测Ni-W合金镀层的硬度和耐腐蚀性能[J].表面技术,2016,45(12):140-146.
ZHOU Qiong-yu,Xie Wei,WANG Xiao-fen,WANG Cao,HU Yi-feng.Artificial Neural Network-based Prediction of Hardness and Corrosion Resistance of Ni-W Alloy Coating[J].Surface Technology,2016,45(12):140-146
基于人工神经网络预测Ni-W合金镀层的硬度和耐腐蚀性能
Artificial Neural Network-based Prediction of Hardness and Corrosion Resistance of Ni-W Alloy Coating
投稿时间:2016-05-08  修订日期:2016-12-20
DOI:10.16490/j.cnki.issn.1001-3660.2016.12.023
中文关键词:  Ni-W合金  镀层  人工神经网络  BP网络  硬度  耐蚀性
英文关键词:Ni-W alloy  coating  artificial neural network  BP network  hardness  corrosion resistance
基金项目:国家自然科学基金(51504104);江西省自然科学基金(20151BAB216012,20161BAB206141); 江西理工大学博士启动基金(3401223204)
作者单位
周琼宇 1.江西理工大学 材料科学与工程学院,江西 赣州 341000;2.上海大学 材料科学与工程学院,上海 200072 
谢蔚 江西理工大学 材料科学与工程学院,江西 赣州 341000 
王小芬 江西理工大学 材料科学与工程学院,江西 赣州 341000 
王操 江西理工大学 材料科学与工程学院,江西 赣州 341000 
胡义锋 江西理工大学 材料科学与工程学院,江西 赣州 341000 
AuthorInstitution
ZHOU Qiong-yu 1.School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China;2. School of Materials Science and Engineering, Shanghai University, Shanghai 200072, China 
Xie Wei School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China 
WANG Xiao-fen School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China 
WANG Cao School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China 
HU Yi-feng School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China 
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中文摘要:
      目的 预测Ni-W合金镀层的硬度和耐腐蚀性能,优化Ni-W合金镀层的电沉积工艺。方法 在柠檬酸-硫酸盐溶液体系中直接沉积制备Ni-W合金镀层,并将实验所得镀层数据作为学习样品,利用BP神经网络对建立了Ni-W合金电沉积过程参数对镀层硬度和腐蚀电流密度之间的映射关系。结果 低碳钢表面所沉积的Ni-W合金镀层表面均匀致密,与基体结合良好,能够有效地对基体起到保护作用。第二隐层的加入使得3-7-15-2四层网络达到网络收敛的训练次数(1 215 365次)远小于3-7-2三层网络的训练次数(239 950 000次)。四层网络预测所得镀层的硬度和腐蚀电流密度与实验值十分相近,其相对误差≤5.03%。结论 BP神经网络能够准确建立电沉积Ni-W合金镀层的工艺条件和目标性能之间的映射关系,在本文所用的沉积体系和参数范围内,Ni-W合金镀层的显微硬度在296~982HV之间,其硬度最大时所对应的电沉积工艺条件为:pH=7.2,电流密度8 A/dm2,WO42+浓度为0.46 mol/L。Ni-W合金镀层的腐蚀电流密度在7.3~100 μA/cm2范围内。镀层耐蚀性能最好时,即镀层腐蚀电流密度最小时的电沉积工艺条件为:pH=6.4,电流密度0.36 A/dm2,WO42+浓度为0.34 mol/L。
英文摘要:
      The work aims to predicte hardness and corrosion resistance so as to optimize the deposition process of electrodeposited Ni-W alloy coating. Ni–W alloy coating was prepared by direct deposition in aqueous citrate-sulphate solution system. Coating statistics obtained by means of experiment shall be taken as samples to be studied. A neural network was used to establish electro-deposition process parameters of Ni-W alloy, so as to reflect mapping relation between coating hardness and corrosion current density. Ni-W alloy coating deposited on surface of low-carbon steel was uniform and compact, provided with good adhesion to the substrate. Hence it could protect the substrate effectively. With the addition of second hidden layer, 3-7-15-2 four-layer network reaches training times of net work convergence (1 215 365 times), far less than that of 3-7-2 three-layer network (239 950 000 times). The predicted values of hardness and corrosion current density (Jcorr) were close to the values got by experiment, and the relative error was≤ 5.03%. An accurate mapping relation between process conditions of electrodeposited Ni-W alloy coating and target properties can be built by BP neural network. The microhardness of Ni–W alloy coating was within 296~982HV. Electrodeposition process conditions corresponding to maximum hardness are as follows: pH value of 7.2, deposition current density of 8 A/dm2 and WO42+ content of 0.46 mol/L. Corrosion current density of the Ni–W alloy coating is within 7.3~100 μA/cm2. Electrodeposition process conditions correponding to lowest corrosion current density, i.e., best corrosion resistance, are as follows: pH value of 6.4, deposition current density of 0.36 A/dm2 and WO42+ content of 0.34 mol/L.
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