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中国腐蚀与防护学报  2017, Vol. 37 Issue (4): 389-394    DOI: 10.11902/1005.4537.2016.064
  研究报告 本期目录 | 过刊浏览 |
人工神经网络在钛合金表面Ni-SiC复合电镀工艺中的应用
郭宝会1,2(), 邱友绪3, 李海龙1
1 渭南师范学院 数理学院 渭南 714099
2 渭南师范学院 陕西省X射线检测与应用研发中心 渭南 714099
3 自贡市高新技术创业服务中心 自贡 643000
Application of Artificial Neural Network for Preparation Process of Ni-SiC Composite Coatings on Ti-Alloy TA15
Baohui GUO1,2(), Youxu QIU3, Hailong LI1
1 School of Physical and Engineering, Weinan Normal University, Weinan 714099, China
2 Shaanxi Research and Development Center of X-ray Detection and Application, Weinan Normal University, Weinan 714099, China
3 Zi-gong Hi-tech Innovation Service Center, Zigong 643000, China
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摘要: 

采用复合电镀在钛合金表面制备了Ni-SiC复合镀层,并利用人工神经网络预测了复合电镀工艺参数对镀层组织结构的影响。结果表明：增加镀液中SiC颗粒含量和搅拌速率均会明显增加复合镀层中SiC的含量,从而增加镀层的硬度;增加阴极电流密度会增加镀层的生长速率,但过高的阴极电流密度导致镀层组织产生裂纹。采用人工神经网络模型对不同复合电镀工艺参数所制备的Ni-SiC复合镀层的厚度和硬度进行了预测,所获得的预测结果与实验结果吻合较好,偏差处于合理范围。
关键词 ： 钛合金,  Ni-SiC复合镀,  人工神经网络,  预测    
Abstract：

Composite coatings of Ni-SiC were prepared on Ti-alloy TA15 by composite electroplating technology, while the effect of electroplating parameters on the coating structure was predicted by means of artificial neural network approach. The results showed that the increase of SiC particles in the plating bath and the stirring speed could lead to higher SiC content of the composite coating, which in turn resulted in higher coating hardness. Increase in cathodic current density caused higher coating growth rates, but too higher cathodic current density would also cause cracks in the coatings. Predictions of the coating growth rates and coating hardness were carried out via artificial neural network. After training, the neural network model was available for the prediction of the thickness and the hardness of the coating.
Key words： Ti alloy    Ni-SiC electroplating    artificial neural network    prediction
收稿日期: 2016-05-16     
ZTFLH:  TG146.2  
基金资助:陕西省自然科学研究项目 (2015KW-022),渭南市自然科学项目 (2015KYJ-2-4) 和渭南师范学院科研项目 (17ZRRC02)
作者简介:

作者简介 郭宝会,男,1978年生,博士,副教授
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引用本文:

郭宝会, 邱友绪, 李海龙. 人工神经网络在钛合金表面Ni-SiC复合电镀工艺中的应用[J]. 中国腐蚀与防护学报, 2017, 37(4): 389-394.
Baohui GUO, Youxu QIU, Hailong LI. Application of Artificial Neural Network for Preparation Process of Ni-SiC Composite Coatings on Ti-Alloy TA15. Journal of Chinese Society for Corrosion and protection, 2017, 37(4): 389-394.

链接本文:

https://www.jcscp.org/CN/10.11902/1005.4537.2016.064      或      https://www.jcscp.org/CN/Y2017/V37/I4/389

Test Factor Test results
SiC content
gL-1		 Stirring speed
rmin-1		 Current density
Adm-2		 Plating bath temp.
℃		 Micro-hardness
HV0.49		 Plating thickness
μm
1 10 50 1 40 422 46
2 10 100 2 50 443 73
3 10 200 3 60 452 90
4 10 400 4 70 459 105
5 30 50 2 60 432 70
6 30 100 1 70 471 49
7 30 200 4 40 512 102
8 30 400 3 50 529 86
9 60 50 3 70 563 87
10 60 100 4 60 632 96
11 60 200 1 50 689 52
12 60 400 2 40 718 75
13 90 50 4 50 592 98
14 90 100 3 40 636 82
15 90 200 2 70 732 68
16 90 400 1 60 738 44
表1  正交试验结果
图1  Ni-SiC复合镀层的表面和截面形貌、表面的XRD谱和沿镀层厚度方向的硬度分布曲线
图2  采用表1中实验2、5和15的参数所制备的Ni-SiC复合镀层的横截面形貌
图3  采用表1中实验8和13的参数所制备的Ni-SiC复合镀层的横截面形貌
图4  神经网络结构
图5  BP神经网络的训练结果
Test Factor Experimental result Prediction result Deviation
SiC contentgL-1 Stirring speed
rmin-1		 Current density
Adm-2		 Plating bath temp.
℃		 Plating thickness
μm		 Micro-
hardness
HV0.49		 Plating thickness μm Micro-
hardness
HV0.49		 Plating thicknessdeviation / % Micro-
hardness
deviation / %
1 20 80 0.5 45 31 446 30 452 -3.2 +1.3
2 40 150 1.5 55 59 507 56 522 -5.1 +2.9
3 80 300 2.5 65 78 746 79 731 +1.2 -2.0
表2  预测与实测结果对比
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