人工神经网络在钛合金表面Ni-SiC复合电镀工艺中的应用  

Application of Artificial Neural Network for Preparation Process of Ni-SiC Composite Coatings on Ti-Alloy TA15

作　　者：郭宝会 邱友绪[3] 李海龙 

机构地区：[1]渭南师范学院数理学院,渭南714099 [2]渭南师范学院陕西省X射线检测与应用研发中心,渭南714099 [3]自贡市高新技术创业服务中心,自贡643000

出　　处：《中国腐蚀与防护学报》2017年第4期389-394,共6页Journal of Chinese Society For Corrosion and Protection

基　　金：陕西省自然科学研究项目(2015KW-022);渭南市自然科学项目(2015KYJ-2-4);渭南师范学院科研项目(17ZRRC02)~~

摘　　要：采用复合电镀在钛合金表面制备了Ni-SiC复合镀层,并利用人工神经网络预测了复合电镀工艺参数对镀层组织结构的影响。结果表明:增加镀液中SiC颗粒含量和搅拌速率均会明显增加复合镀层中SiC的含量,从而增加镀层的硬度;增加阴极电流密度会增加镀层的生长速率,但过高的阴极电流密度导致镀层组织产生裂纹。采用人工神经网络模型对不同复合电镀工艺参数所制备的Ni-SiC复合镀层的厚度和硬度进行了预测,所获得的预测结果与实验结果吻合较好,偏差处于合理范围。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.

关 键 词：钛合金 Ni-SiC复合镀 人工神经网络 预测 

分 类 号：TG146.2[一般工业技术—材料科学与工程]