A series of ball–disk contact friction tests were carried out using a microtribometer to study the tribological characteristics of steel/steel rubbing pairs immersed in 47 different organic compounds as lubricant base oils. The structures and their friction data were included in a back-propagation neural network (BPNN) quantitative structure tribo-ability relationship (QSTR) model. Following leave-one-out (LOO) cross-validation, the BPNN model shows good predictability and accuracy for the friction parameter (R2 = 0.994, R2(LOO) = 0.849, and q2 = 0.935). Connectivity indices (CHI) show the large positive contribution to friction, which imply that friction performance has a strong correlation with molecular structure. The BPNN–QSTR models can flexibly and easily estimate the friction properties of lubricant base oils.
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July 2016
Research-Article
BPNN–QSTR Friction Model for Organic Compounds as Potential Lubricant Base Oils
Xinlei Gao,
Xinlei Gao
School of Chemical and
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
e-mail: gaoxl0131@163.com
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
e-mail: gaoxl0131@163.com
Search for other works by this author on:
Ruitao Wang,
Ruitao Wang
School of Chemical and
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
Search for other works by this author on:
Zhan Wang,
Zhan Wang
School of Chemical and
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
Search for other works by this author on:
Kang Dai
Kang Dai
College of Pharmacy,
South-Central University for Nationalities,
Wuhan, Hubei 430074, China
South-Central University for Nationalities,
Wuhan, Hubei 430074, China
Search for other works by this author on:
Xinlei Gao
School of Chemical and
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
e-mail: gaoxl0131@163.com
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
e-mail: gaoxl0131@163.com
Ruitao Wang
School of Chemical and
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
Zhan Wang
School of Chemical and
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China
Kang Dai
College of Pharmacy,
South-Central University for Nationalities,
Wuhan, Hubei 430074, China
South-Central University for Nationalities,
Wuhan, Hubei 430074, China
1Corresponding author.
Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received March 27, 2015; final manuscript received December 2, 2015; published online March 22, 2016. Assoc. Editor: Ning Ren.
J. Tribol. Jul 2016, 138(3): 031801 (8 pages)
Published Online: March 22, 2016
Article history
Received:
March 27, 2015
Revised:
December 2, 2015
Citation
Gao, X., Wang, R., Wang, Z., and Dai, K. (March 22, 2016). "BPNN–QSTR Friction Model for Organic Compounds as Potential Lubricant Base Oils." ASME. J. Tribol. July 2016; 138(3): 031801. https://doi.org/10.1115/1.4032304
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