Application of Artificial Neural Networks to Optimize Processing - Properties of Ni-Tic Composite Coatings

Han Jun Wei; Wan Chang Sun; Guan Qun Hou; Feng Zhang; Qin Shi

doi:10.4028/www.scientific.net/MSF.817.725

Paper Titles

Finite Element Analysis of the Influence of Crack Tip Stress Distributionon Fatigue Crack Propagation Rate of 5XXX Aluminum Alloy
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Strain Fields around Dislocation Cores Studied by Analyzing Coordinates of Discrete Atoms
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First-Principles Study of Structural and Thermal of α-Ni₄N
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Application of Artificial Neural Networks to Optimize Processing - Properties of Ni-Tic Composite Coatings
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Mesoscopic Simulation of the Ferrite Nucleation on Austenitic Grain Boundary for Nanograined Steel
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Simulating Structural Transformation of a Cu₁₅₅₃ Nanoparticle on Heating at Atomic Scale
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Thermal Properties and Transition Temperatures of AlB₂-Type Diborides
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Modeling of Flow Stress and Grain Size Based on Z Parameter and Material Constants of Al-5.2Mg-0.6Mn Alloy during Hot Deformation
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HomeMaterials Science ForumMaterials Science Forum Vol. 817Application of Artificial Neural Networks to...

Application of Artificial Neural Networks to Optimize Processing - Properties of Ni-Tic Composite Coatings

Abstract:

The plating parameters for optimizing the wear and corrosion resistance of Ni-TiC composite coatings were selected by orthogonal test, mainly including the TiC particles concentration, current density, duty cycle, frequency and stirring rate. A three-layer BP (Back Propagation) neural network with Lavenberg-Marquardt algorithm was established by MATLAB, which was used to train the network and predicted orthogonal experimental data. In addition, the best parameters combination of the composite coating were predicted and verified by experiments. The results predicted through the proposed BP model are in good agreement with the experimental values, the relative error is small, and the maximum error is less than 3% and the coefficient of determination value is 0.9997.

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Periodical:

Materials Science Forum (Volume 817)

Pages:

725-730

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.817.725

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Online since:

April 2015

Authors:

Han Jun Wei, Wan Chang Sun*, Guan Qun Hou, Feng Zhang, Qin Shi

Keywords:

BP Neural Network, Composite Coating, Orthogonal Test, Pulse Electrodeposition

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* - Corresponding Author

References

[1] Shi Xiao Xia, Chen Yi Ming, Li Zhi Ju. Neural Networks for Applied Sciences and Engineering-From Fundamentals to Complex Pattern Recognition[M]. Beijing: China Machine Press. 2010(in Chinese).

Google Scholar

[2] Lu Wei. Study of Nano-Composite Cu-A12O3 Coatings by Jet Electrodeposition Based on Neural Network[J]. Electroplating & Pollution Control. 2008(1): 20-23(in Chinese).

Google Scholar

[3] Zhang Wen Feng, Zhu Di, Zeng Yong Bin. Study of optimizing process parameters about Ni-SiC composite electrodeposition based on BP neural network[J]. Journal of Functional Materials. 2004(35): 383-388(in Chinese).

Google Scholar

[4] Wang Xiao Chuan, Shi Feng, Yu Lei, Li Yang. MATLAB Neural Network Analysis of 43 Cases[M]. Beijing: Beihang University Press, 2013(in Chinese).

Google Scholar

[5] S.C. Vettivel, N. Selvakumar, N. Leema. Experimental and Prediction of Sintered Cu–W Composite by Using Artificial Neural Networks[M]. Materials and Design. 2013(45): 323-335.

DOI: 10.1016/j.matdes.2012.08.056

Google Scholar

[6] Selvakumar N, Radha P, Narayanasamy R, Joseph Davidson M. Prediction of deformation characteristics of sintered aluminum performs using neural networks. Modell Simul Mater Sci Eng. 2004(12): 6611-6620.

DOI: 10.1088/0965-0393/12/4/004

Google Scholar

[7] Dong Chang Hong. The Application of Matlab Neural Network[M]. Beijing: National Defense Industry Press. 2007(in Chinese).

Google Scholar