Abstract
In order to improve the tribological performance of camshaft bearings, a design method based on NSGA-II and TOPSIS decision methods was proposed. The structural-performance parameters sample dataset was obtained. The multi-objective optimization genetic algorithm and multi-criteria decision-making method were used to optimize the bearings structure with the goal of minimizing the total friction loss and the maximum wear height, as well as maximizing the average values of minimum oil film thickness. The optimal performance and structural parameters of camshaft bearings obtained through multi-objective optimization strategy have obvious directionality. The entropy weighted TOPSIS multi-criteria decision-making method effectively obtained the optimal solution. Compared with the original structure, the optimized structure significantly reduces the total friction loss and maximum wear height.
Original language | English |
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Article number | 21 |
Journal | Structural and Multidisciplinary Optimization |
Volume | 68 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2025 |
Keywords
- Camshaft bearing
- Deep neural network
- Friction loss
- Multi-objective optimization
- Wear