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PREDICTION OF CONTACT DISTRIBUTION ON ROUGH SURFACES USING DEEP LEARNING ALGORITHMS
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DOI：10.22190/FUME250307026J.png)
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Abstract
En 中文
The contact characteristics of rough surfaces play a crucial role in determining friction, wear, thermal resistance, and electrical conductivity. This study proposes a deep learning approach to efficiently predict the contact distribution of rough surfaces based on surface image information alone, and evaluates its effectiveness against numerical methods. A U-Net architecture was employed for predicting contact areas under varying scales and load conditions, using a dataset of 100,000 fractal surfaces generated via the random midpoint displacement (RMD) method. The results indicate that the deep learning model achieved performance comparable to conventional numerical methods in predicting both contact areas and electrical contact resistance, with minimal error observed in electrical contact resistance prediction. The model approached the contact prediction as an image segmentation task, enabling faster and more efficient computations than traditional numerical approaches. High performance across metrics such as Dice coefficient, Jaccard index, Bradford Factor (BF) score, and pixel accuracy highlighted its ability to maintain prediction accuracy while significantly enhancing computational efficiency. Additionally, by leveraging two-dimensional (2D) fast Fourier transform (FFT) techniques, the model effectively captured both low-and high-frequency characteristics, accurately predicting large-scale and fine-scale features of contact areas, while reducing computation time by more than 95% compared to numerical models. These findings demonstrate that the deep learning algorithms can effectively address multiscale contact problems, offering reliable data for various engineering design applications, including friction, wear, and thermal/electrical resistance, as well as enabling real-time analysis and large-scale simulations.
Keywords:
Contact distribution
Fractal rough surfaces
Deep Learning
U-Net
Scale variations
Force variations
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