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Volume 11
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Article

A BPNN-QSTR Model for Friction-Reducing Performance of Organic Liquid Lubricants on SiC/PI Friction Pair

by
Tingting Wang
1,
Liang Zhang
1,
Hao Chen
1,
Li Wu
2 and
Xinlei Gao
3,*
1
School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan 430023, China
2
School of Chemistry and Environmental Engineering, Wuhan Institute of Technology, Wuhan 430074, China
3
School of Materials Science and Engineering, Hubei University, Wuhan 430062, China
*
Author to whom correspondence should be addressed.
Lubricants 2023, 11(9), 387; https://doi.org/10.3390/lubricants11090387
Submission received: 31 July 2023 / Revised: 27 August 2023 / Accepted: 8 September 2023 / Published: 10 September 2023
(This article belongs to the Special Issue Green Tribology: New Insights toward a Sustainable World 2023)
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Abstract

In this study, a systematic test of 36 organic liquid compounds as lubricants in the SiC/PI friction pair was conducted to investigate their friction-reducing performance. The back propagation neural network (BPNN) method was employed to establish a quantitative structure tribo-ability relationship (QSTR) model for the friction performance of these lubricants. The developed BPNN-QSTR model exhibited excellent fitting and predictive accuracy, with R2 = 0.9700, R2 (LOO) = 0.6570, and q2 = 0.8606. The impact of different descriptors in the model on the friction-reducing performance of the lubricants was explored. The results provide valuable guidance for the design and optimization of lubricants in SiC/PI friction systems, contributing to the development of high-performance lubrication systems.
Keywords: organic liquid lubricants; quantitative structure tribo-ability relationship; back propagation neural network; friction-reducing performance organic liquid lubricants; quantitative structure tribo-ability relationship; back propagation neural network; friction-reducing performance

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MDPI and ACS Style

Wang, T.; Zhang, L.; Chen, H.; Wu, L.; Gao, X. A BPNN-QSTR Model for Friction-Reducing Performance of Organic Liquid Lubricants on SiC/PI Friction Pair. Lubricants 2023, 11, 387. https://doi.org/10.3390/lubricants11090387

AMA Style

Wang T, Zhang L, Chen H, Wu L, Gao X. A BPNN-QSTR Model for Friction-Reducing Performance of Organic Liquid Lubricants on SiC/PI Friction Pair. Lubricants. 2023; 11(9):387. https://doi.org/10.3390/lubricants11090387

Chicago/Turabian Style

Wang, Tingting, Liang Zhang, Hao Chen, Li Wu, and Xinlei Gao. 2023. "A BPNN-QSTR Model for Friction-Reducing Performance of Organic Liquid Lubricants on SiC/PI Friction Pair" Lubricants 11, no. 9: 387. https://doi.org/10.3390/lubricants11090387

APA Style

Wang, T., Zhang, L., Chen, H., Wu, L., & Gao, X. (2023). A BPNN-QSTR Model for Friction-Reducing Performance of Organic Liquid Lubricants on SiC/PI Friction Pair. Lubricants, 11(9), 387. https://doi.org/10.3390/lubricants11090387

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