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 |
|
| Author | Institution |
| 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 |
|
| 摘要点击次数: |
| 全文下载次数: |
| 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. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
渝公网安备 50010702501715号