Data-Driven Multi-Objective Optimization Design of Micro-Textured Wet Friction Pair

Yulin Xiao, Donghui Chen, Shiqi Hao, Chong Ning, Xiaotong Ma, Bingyang Wang and Xiao Yang ()
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Yulin Xiao: Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
Donghui Chen: Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
Shiqi Hao: Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
Chong Ning: Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
Xiaotong Ma: Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
Bingyang Wang: Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
Xiao Yang: Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China

Agriculture, 2025, vol. 15, issue 20, 1-19

Abstract: Friction pairs in heavy-duty power-shift tractor wet clutches operate under complex conditions, making them vulnerable to damage and reducing reliability. Optimizing their tribological performance requires a trade-off between a high coefficient of friction (COF) for torque transmission and a low temperature rise ( ∆ T ) to prevent thermal damage. Surface texturing is an effective method for improving the tribological performance of friction pairs. This study simulated the friction of wet clutch pairs via pin-on-disk tests and designed micro-textures on the pin surface to enhance tribological performance. Based on the experimental data, a Gaussian Process Regression (GPR) surrogate model was developed to accurately predict COF and ∆ T as a function of the clutch’s operating and micro-texture’s geometric parameters. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was then employed to obtain the optimal set of solutions. The obtained pareto front clearly revealed the COF–temperature rise trade-off. From the optimal solution set, optimal micro-texture parameters for two typical operating conditions of different clutches were extracted. Compared with the untextured surface, the optimal solutions increased COF by 2.6%/1.2% and reduced ∆ T by 39.2%/12.1%. Relative to neighboring experimental points, COF further increased by 11.3%/2.7% and ∆ T decreased by 16.6%/1.7%. This work establishes a method for balancing the frictional and thermal performance of friction pairs.

Keywords: texture; tribology; wet clutch; gaussian process regression; multi-objective particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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