Abstract
Running-in quality can be improved by optimizing the running-in parameters (load, speed, and running-in time). The relationship between running-in quality and the fractal dimensions of friction signal and wear surface is analyzed. It shows that the larger the fractal dimension, the better the running-in quality. A multi-objective optimization design of the running-in parameters of the main bearing was carried out using a non-dominated sorting genetic algorithm with an elite strategy. The optimization targets are the large fractal dimension of coefficient of friction (COF), the large fractal dimension of wear surface, and short running-in time. It shows that the selection principles of running-in parameters are different for different stages and priorities. The optimal running-in parameters listed in this paper provide a specific reference to the optimal design of the three-stage running-in of main bearing.
Issue Section:
Friction & Wear
Keywords:
multi-objective optimization,
three-stage running-in,
running-in quality,
fractal dimension,
bearing design and technology,
journal bearings,
tribological systems
Topics:
Bearings,
Dimensions,
Fractals,
Friction,
Optimization,
Pareto optimization,
Signals,
Stress,
Wear,
Diesel engines,
Stability
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