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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

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Ruitao Wang,

Ruitao Wang

School of Chemical and
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China

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Zhan Wang,

Zhan Wang

School of Chemical and
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China

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Kang Dai

Kang Dai

College of Pharmacy,
South-Central University for Nationalities,
Wuhan, Hubei 430074, China

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Author and Article Information

Xinlei Gao

School of Chemical and
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

Zhan Wang

School of Chemical and
Environmental Engineering,
Wuhan Polytechnic University,
Wuhan, Hubei 430023, China

Kang Dai

College of Pharmacy,
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)

Paper No: TRIB-15-1093 https://doi.org/10.1115/1.4032304

Published Online: March 22, 2016

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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