Effect And Prediction Of Ionic Liquid Modified Nano Metal Oxides As Additives On The Tribological Properties Of Lubricating Greases

With the rapid rise of the national economy and the rapid development of the secondary and tertiary industries,along with the surge in electricity consumption,the scale of the State Construction Power Grid has increased in order,followed by problems such as wear and heat generated in the electrical contact process of power grid equipment.Therefore,there is an urgent demand for excellent antifriction,antiwear and corrosionresistant conductive grease,while the existing grease in the market cannot meet the demand of UHV lines.In this paper,the current carrying tribological properties of polyurea grease after modification of nano-metal oxides and ionic liquids are studied.The contents and results are as follows:1.A non-soap polyurea grease was prepared with three nano-oxides of tin dioxide,indium tin oxide and tin antimony oxide as additives,PAO as base oil,octadecylamine and toluene isocyanate as thickeners.The friction and lubrication ability under non-currentcarrying conditions and conductive friction ability under current-carrying conditions were investigated.The experimental results show that three kinds of nano-metal oxides as additives exhibit good tribological properties and conductivity under current carrying conditions,among which ITO and ATO have the best lubricating properties and conductivity.2.Using ionic liquid l-butyl-3-methylimidazolium tetrafluoroborate(L-B104),1butyl-3-methylimidazolium lactate(L-L104)and 1-octyl-3-methylimidazolium tetrafluoroborate(L-B108)as modifiers,the three metal oxides were modified by mechanical grinding with the ratio of nano-metal oxide to ionic liquid at 8:2.The results show that the modified additives exhibit excellent tribological properties and electrical conductivity,and the comprehensive properties of nano-metal oxides modified by lactate ionic liquid are the best.3.According to the design principle of the optimum concentration of additives in the process of lubricating grease compounding,combined with machine learning and experimental scheme design,a multiple linear regression model is proposed,and a normalized treatment model is adopted to solve the problem of how the friction coefficient and the wear mark width are the same dimension.Under the condition that the weight of friction coefficient and wear scar width are the same,the search for the best additive concentration is completed,and the error is within 5%compared with the actual verification result.It is proved that the normalized regression model can be used to search for the optimum additive concentration of lubricating grease.