Title :
Optimising of Micro Electroforming Ni-Fe Alloy Process for Microstructure
Author :
Zheng, Xiaohu ; Liu, YuanWei
Author_Institution :
Fac. of Mech. Eng., Huaiyin Inst. of Technol., Huaiyin, China
Abstract :
The relationship between the coating composition, microstructure and the main process parameters of Micro electroforming Ni-Fe alloys was studied in this paper. Orthogonal experimental design was applied in research. From experiment it can be concluded that the soft bake temperature and time was the key factor of the structure quality. In order to obtain the suitable parameters of the deposit, an artificial neural network (ANN) with 3 layers were built. The ANN was trained based on orthogoality experiment using back propagation algorithm. Compared to the experiment results, the prediction error was less than 2.0%, which proved that the ANN was effective. The characteristics of the micro electroforming process were analysed systematically. And the optimal process parameters to obtain Ni-20%Fe deposition was as following: FeSO4middot7H20 concentration: 5.5g/L; PH value of the solution: 2.5; current density: 3.5 A/dm2; electrolyte temperature: 55degC. The results indicate that the Ni-Fe deposit is bright and compact. Electrodeposited Ni-20%Fe has a strong paramagnetic effect with the smallest value of remanence 0.036 mAmiddotm2 and the coercivity:0.187 kA/m The Ni-Fe micro electroforming process for the fabrication of microstructure was optimised.
Keywords :
backpropagation; crystal microstructure; electroforming; metallurgical industries; nickel alloys; artificial neural network; backpropagation algorithm; coating composition; current density; electrolyte temperature; microelectroforming Ni-Fe alloy process; microelectroforming process; microstructure; optimal process parameter; orthogonal experimental design; soft bake temperature; structure quality; Anodes; Artificial neural networks; Current density; Design for experiments; Iron alloys; Magnetic materials; Microstructure; Nickel alloys; Perpendicular magnetic recording; Temperature; Composition; Microstructure; Ni-Fe deposit; Optimisation process; Prediction model;
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Print_ISBN :
978-0-7695-3804-4
DOI :
10.1109/ICICTA.2009.519