Friction behavior and wear protection ability of selected base lubricants
被引量：2
Friction behavior and wear protection ability of selected base lubricants
friction
lubricants
viscosity
Stribeck curves
wear loss
同被引文献13
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1夏延秋,徐大祎,冯欣,蔡美荣.基于极限学习机和优化算法的润滑油添加剂种类识别与含量预测[J].摩擦学学报,2020,40(1):97-106. 被引量：9
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2葛翔宇,夏延秋,冯欣,宋玉旺,樊小强.锂盐型电力复合脂的导电性和摩擦学性能[J].机械工程学报,2015,51(15):61-66. 被引量：20
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3李晓鹤,冯欣,夏延秋.布谷鸟搜索的润滑脂特征红外光谱波段优选技术[J].光谱学与光谱分析,2017,37(12):3703-3708. 被引量：6
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4Igor E.UFLYAND,Vladimir A.ZHINZHILO,Victoria E.BURLAKOVA.Metal-containing nanomaterials as lubricant additives: State-of-the-art and future development[J].Friction,2019,7(2):93-116. 被引量：21
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5Le JIN,Herbert SCHEERER,Georg ANDERSOHN,Matthias OECHSNER,Dieter HELLMANN.Experimental study on the tribo-chemical smoothening process between self-mated silicon carbide in a water-lubricated surfacecontact reciprocating test[J].Friction,2019,7(2):181-191. 被引量：2
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6Osman ALTAY,Turan GURGENC,Mustafa ULAS,Cihan OZEL.Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms[J].Friction,2020,8(1):107-114. 被引量：7
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7Mustafa ULAS,Osman ALTAY,Turan GURGENC,Cihan OZEL.A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic,kernel-based,and weighted extreme learning machine[J].Friction,2020,8(6):1102-1116. 被引量：5
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8Deepak K.PRAJAPATI,Mayank TIWARI.The correlation between friction coefficient and areal topography parameters for AISI 304 steel sliding against AISI 52100 steel[J].Friction,2021,9(1):41-60. 被引量：2
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9Gabriele RIVA,Francesco VARRIALE,Jens WAHLSTRÖM.A finite element analysis(FEA)approach to simulate the coefficient of friction of a brake system starting from material friction characterization[J].Friction,2021,9(1):191-200. 被引量：1
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10郭守敬,白天忠,梁雪美,安良成,张安贵.不同基础油之间调配及在柴油机油中应用研究[J].炼油技术与工程,2021,51(9):53-56. 被引量：1
引证文献2
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1Josef PROST,Guido BOIDI,Thomas LEBERSORGER,Markus VARGA,Georg VORLAUFER.Comprehensive review of tribometer dynamics-Cycle-based data analysis and visualization[J].Friction,2022,10(5):772-786.
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2夏延秋,王裕兴,冯欣,蔡美荣.群智能搜索在基础油性能预测模型中的优化效能[J].摩擦学学报,2023,43(4):429-438. 被引量：1
