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Journal of Chinese Society for Corrosion and protection  2017, Vol. 37 Issue (4): 389-394    DOI: 10.11902/1005.4537.2016.064
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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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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     
Received:  16 May 2016     
ZTFLH:  TG146.2  
Fund: Supported by Shaanxi Natural Science Fund (2015KW-022), Weinan Natural Science Fund (2015KYJ-2-4) and Weinan Normal University Natural Science Fund (17ZRRC02)
About author: 

These authors contributed equally to this work.
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Baohui GUO
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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.

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https://www.jcscp.org/EN/10.11902/1005.4537.2016.064     OR     https://www.jcscp.org/EN/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
Table 1  Orthogonal test results
Fig.1  Surface (a) and cross-sectional (b) BSE images, XRD pattern (c) and micro-hardness distribution profile (d) of the Ni-SiC composite coating prepared under the plating condition of test 12 in Table 1
Fig.2  Cross-sectional BSE images of the Ni-SiC composite coatings prepared using the plating parameters of test 2 (a), test 5 (b) and test 15 (c) in Table 1
Fig.3  Cross-sectional BSE images of the Ni-SiC composite coatings prepared using the plating parameters of tests 8 (a) and 13 (b) in Table 1
Fig.4  Structure diagram of the BP neural network
Fig.5  Training process of the sample using BP neural network
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
Table 2  Comparison between the prediction and experimental results
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