Fractal Characterization Of Rough Surface And Its Impact On The Interfacial Adhesion Property

Equipment requires cleaning before start up or restart up after maintenance period to ensure the equipment operates safety and with a high efficiency. The essence of equipment cleaning process is to reduce the adhesion of dirt particle and surface of material. Surface property of material has a great impact on the interfacial adhesion of particle and surface, and surface roughness is one particularly crucial factor. A quantitative and reasonable characterization of surface roughness will enable us have a deep understanding of the interaction between dirt particle and rough surface, especially when we establish a relationship between this parameter and interfacial adhesion. Also it can provide basic theory for surface decontamination.Due to the self-similarity of rough surface, fractal theory can be used to achieve effective characterization of surface topography. Exact solution of fractal parameters depend on the accurate identification of fractal scaling region. Therefore, a new method based on density peaks clustering algorithm to identify the scaling region is proposed, then was validated through determining surface fractal of milling, gouging and lapping experiments. Results showed that this novel method can distinguish the scaling region automatically and quickly, taking a short running time especially for data with large amount and high-density.Traditional indicators have the feature of size scale correlation, while fractal indicators has just the opposite feature. However, in practical characterization of rough surface, while size scale feature of both indicators are consistent with the theory need to be verified by experiments. In this paper, rough surfaces manufactured by milling, gouging and lapping are used for studying size scale feature of above two indicators. The results show that traditional indicators have the feature of size scale correlation in any condition, which consistent with theory, while fractal indicators do not have the feature of scale independence under most conditions, which has a discrepancy with theory. It can conclude from the above study of size scale feature of fractal indicators that there exists unstable and stable fractal region when fractal indicators change with scale. Therefore, if we want to use fractal theory to characterize the topography of rough surface, the most important is to find the critical point of those two region. In the analyzing process the two-dimensional surface contour, we transfer the finding of critical point into solving the quantitative relationship of sampling length and angle ?. By the quantitative relationship, we can directly make angle ? be 0 to obtain the critical sampling length. As long as the sample length is greater than the critical length, stable fractal region can be obtained, and then we can calculate the stable fractal dimension, which will not affected by the resolution of instrument. In this case the fractal dimension can reflect the inherent property of rough surface, which can be used as an accurate characterization of surface roughness.Through the analysis of cleaning mechanism of solid dirt, we find that surface roughness has a great impact on interfacial adhesion. The results show that adhesion force changes with surface roughness, Ra<0.4627?m, adhesion force decreases with the increase of surface roughness; 1.3461?m<Ra<2.9755?m, adhesion force increases gradually; Ra>3?m, adhesion force remains the same. Adhesion force also changes with fractal indicator, it changes the opposite trend with surface roughness.