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A Quantitative Structure Tribo-Ability Relationship Model for Ester Lubricant Base Oils

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成果类型:
期刊论文
作者:
Gao, Xinlei*;Wang, Zhan;Dai, Kang;Wang, Tingting
通讯作者:
Gao, Xinlei
作者机构:
[Wang, Tingting; Gao, Xinlei; Wang, Zhan] Wuhan Polytech Univ, Sch Chem & Environm Engn, Wuhan 430023, Hubei Province, Peoples R China.
[Dai, Kang] South Cent Univ Nationalities, Coll Pharm, Wuhan 430074, Hubei Province, Peoples R China.
通讯机构:
[Gao, Xinlei] W
Wuhan Polytech Univ, Sch Chem & Environm Engn, Wuhan 430023, Hubei Province, Peoples R China.
语种:
英文
关键词:
quantitative structure tribo-ability relationship;back-propagation neural network;lubricant base oil;antiwear
期刊:
Journal of Tribology
ISSN:
0742-4787
年:
2015
卷:
137
期:
2
页码:
021801
基金类别:
National Basic Research Program of China (973 Program)National Basic Research Program of China [2013CB632303]; National Nature Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [51075309]
机构署名:
本校为第一且通讯机构
院系归属:
化学与环境工程学院
摘要:
Friction tests with point-point contact were carried out using a microtribometer to investigate the tribological characteristics of steel/steel rubbing pair immersed in 57 kinds of esters as lubricant base oils. A set of 57 esters and their wear data were included in the back-propagation neural network (BPNN)-quantitative structure tribo-ability relationship (QSTR) model with two-dimensional (2D) and three-dimensional (3D) QSTR descriptors. The predictive performance of the BPNN-QSTR model is acceptable. The findings of the BPNN-QSTR model show...

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