| 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],45(12):140-146 |
| Artificial Neural Network-based Prediction of Hardness and Corrosion Resistance of Ni-W Alloy Coating |
| Received:May 08, 2016 Revised:December 20, 2016 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2016.12.023 |
| KeyWord:Ni-W alloy coating artificial neural network BP network hardness corrosion resistance |
| Author | Institution |
| ZHOU Qiong-yu |
1.School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou , China;2. School of Materials Science and Engineering, Shanghai University, Shanghai , China |
| Xie Wei |
School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou , China |
| WANG Xiao-fen |
School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou , China |
| WANG Cao |
School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou , China |
| HU Yi-feng |
School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou , China |
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| Abstract: |
| 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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