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| Prediction of Corrosion Resistance of Ni-ZrO2 Nano-Plating Based on Artificial Neural Network |
| Received:October 14, 2021 Revised:November 19, 2021 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2022.02.016 |
| KeyWord:electrodeposition Ni-ZrO2 nano-plating GRNN neural network BP neural network self-corrosion current density prediction |
| Author | Institution |
| DENG Yu |
School of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing , China;Key Laboratory of Marine Environmental Corrosion and Bio-fouling, Institute of Oceanology, Chinese Academy of Sciences, Qingdao , China;Open Studio for Marine Corrosion and Protection, Pilot National Laboratory for Marine Science and Technology Qingdao, Qingdao , China |
| ZHANG Jie |
School of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing , China;Key Laboratory of Marine Environmental Corrosion and Bio-fouling, Institute of Oceanology, Chinese Academy of Sciences, Qingdao , China;Open Studio for Marine Corrosion and Protection, Pilot National Laboratory for Marine Science and Technology Qingdao, Qingdao , China;Center for Ocean Mega-science, Chinese Academy of Sciences, Qingdao , China |
| PENG Zhong-bo |
School of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing , China |
| XU Wei-chen |
Key Laboratory of Marine Environmental Corrosion and Bio-fouling, Institute of Oceanology, Chinese Academy of Sciences, Qingdao , China;Open Studio for Marine Corrosion and Protection, Pilot National Laboratory for Marine Science and Technology Qingdao, Qingdao , China |
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| Abstract: |
| The work aims to predict the corrosion resistance of Ni-ZrO2 nano-plating and optimize the plating process parameters. Ni-ZrO2 nano-plating was prepared on the surface of cobalt-nickel-based model alloy samples by magnetic stirring-assisted electrodeposition. According to the electroplating process conditions, orthogonal experiments were set up, and each group of experimental plating was electrochemically tested to analyze the corrosion resistance with different process conditions. The three plating process parameters of ZrO2 particle concentration, plating solution temperature and plating current density were used as the input layer of the neural network, and the self-corrosion current density was used as the output layer. The GRNN neural network and BP neural network models were used to predict the corrosion resistance of Ni-ZrO2 nano-plating. When the mass concentration of ZrO2 particles is 6 g/L, the temperature of the plating solution is 60 ℃, and the plating current density is 5 A/dm2, the performance of the Ni-ZrO2 nano-plating is good, showing a small self-corrosion current density. The factors affecting the self-corrosion current density of Ni-ZrO2 nano-plating should meet the following requirements ZrO2 particle concentration>plating solution temperature> plating current density. The average relative errors of the four groups of non-orthogonal experiments predicted by GRNN neural network and BP neural network are 5.30% and 10.74%, respectively. The neural model can effectively predict the corrosion resistance of the plating under different process parameters, thereby optimizing the process parameters and improving the experimental efficiency. In the case of fewer training samples, the prediction performance of the GRNN neural network is more accurate. |
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