基于BP神经网络的Ni-SiC复合电沉积工艺参数优选研究

在复合电沉积工艺中，通过优选工艺参数可以获得纳米颗粒复合量较高的复合沉积层，以使其具有某些特殊的性能。本文运用正交试验法优化了对复合沉积层中纳米颗粒复合量有较大影响的各工艺参数，然后用BP神经网络分析方法对其结果进行分析处理。预测并得到较正交试验法所得最优工艺水平组合时复合量更高的复合沉积层，同时对所得复合沉积层进行了表面形貌观察和能谱分析。

Study of optimizing process parameters about Ni-SiC composite electrodeposition based on BP neural network

In the composite electrodeposition, higher nanoparticle content in nanocomposite coatings were gained by optimizing process parameters, so that these composite coatings have excellent properties. In this paper, the optimized process parameters that have major influence on nanoparticle content were obtained by orthogonal test, and the result was further analyzed by BP neural network. Nanoparticle content in nanocomposite coatings was predicted and the composite coating which has the highest nanoparticle content was gained, and the surface morphology of the nanocomposite coating was surveyed and its energy spectrum was analyzed.