摘要
采用氧-乙炔焰喷熔工艺制备了碳化钨(WC)颗粒增强镍基合金喷熔层,研究了它的腐蚀磨损行为.结果表明:喷熔层耐腐蚀磨损性能随WC含量增加而提高,WC含量在20%~30%范围内,喷熔层耐腐蚀磨损性能最佳,超过30%时,其耐腐蚀磨损性能下降.载荷增加,腐蚀磨损率增大;速度增加,腐蚀磨损率下降.低速重载荷时,WC颗粒增强效果明显,且含30%WC喷熔层耐腐蚀磨损性能最好;高速轻载荷时,因WC原电池效应显著,WC颗粒增强效果减弱.基于人工神经网络的喷熔层腐蚀磨损行为预测与实验结果吻合较好,对喷熔层的应用具有重要指导作用.
Ni-based alloy coatings reinforced by WC particles were prepared by oxygen-acetylene spray-welding technology. The corrosive wear behavior of the coatings was investigated. The results show that the resistance of coating against corrosive wear increases with the increasing content of WC particles. The coatings have excellent anti-wear performance when WC content is between 20 % and 30 %. The corrosive wear rates of the coatings increase as loads increase and they decrease with sliding speeds. At a low speed or heavy load, 30 % WC composite coating has the best wear resistance due to the remarkable reinforcing effect of WC particles, but at high speed or light load, the wear rate increases because of galvanicbattery effect of WC. The prediction results of the corrosive wear behavior of coatings based on artificial neural network (ANN) corresponds with the experimental results, which has an important guiding effect on the application of coatings.
出处
《机械制造与自动化》
2005年第6期55-57,共3页
Machine Building & Automation
关键词
WC
镍基喷熔层
腐蚀
磨损
人工神经网络
WC
nickel-based spray-welding coating
corrosion
wear
artificial neural network