摘要
为提高车轴用材料35CrMo的耐磨性,在Ni60A中添加粒度均为(-280~340)目的二硼化钛TiB2钴Co和铬Cr粉;将等离子喷涂和均匀设计方法引入轴减磨抗磨设计中,在35CrMo上等离子喷涂制备200μm镍60A基二硼化钛TiB2、钴Co、铬Cr复合自润滑涂层。研究结果表明:由SEM可看出涂层与基体结合良好且涂层呈层状结构分布,由EDS可看出各元素渗透到了Ni60A基体里并产生了冶金结合;将验证组结果和神经网络预测值对比,磨损误差在12%之内,显微硬度误差在11%之内,涂层相比基体耐磨性提高了6倍,显微硬度提高了3倍;可从人工神经网络的预测结果中选出具有优良性能的镍60A基二硼化钛TiB2、钴Co、铬Cr复合自润滑涂层的配比范围。
It is the uniform design method to introduced in the design of shaft anti-friction and anti-wear in order to improve the axle material with the wear resistance of thirty-five cobalt molybdenum by plasma spraying,added in the Ni60A by electron microscope granularity are from negative two hundreds eighty to three hundredsforty mesh titanium diboride、chromium and cobaltr powder;plasma spraying preparation including two hundreds micron in thirty-five cobalt molybdenum nickel-based titanium diboride cobaltr composite self-lubricating coatings;The results show that it can see coating and substrate are distributed in combined with good and coating layer structure by electron microscope,you can see permeated each element Ni60A matrix and metallurgical combination by the EDS;and it will verify that the set of results and comparison of the neural network predictive value,wear error within twelvepercent,and microhardness error within eleven percent.the coating matrix wear resistance than improved to six times,and microhardness increased three times.It can be selected in the predictive results of the artificial neural network has excellent properties of nickel-based titanium diboride cobalt composite self-lubricating coating mixture ratio.
作者
崔良
朱魏巍
顾小龙
陈卓君
CUI Liang;ZHUWei-wei;GU Xiao-long;CHEN Zhuo-jun(Zhejiang Province Key Laboratory of Soldering&Brazing Materials and Technology,Zhejiang Hangzhou310011,China;School of Automobile and Transportation,Shenyang Ligong University,Liaoning Shenyang110159,China)
出处
《机械设计与制造》
北大核心
2019年第9期197-200,共4页
Machinery Design & Manufacture
基金
浙江省钎焊材料与技术重点实验室开放基金项目资助(1403)
关键词
镍基自润滑复合涂层
等离子喷涂
耐磨性
显微硬度
组织形貌
Nicke-Based Self-Lubricatingcomposite Coatings
Plasma Spraying
Wear Resistance
Micro-Hardness
Tissue Morphology
