The existing surface roughness standards comprise only two dimensions. However, the real roughness of the surface is 3D （three-dimensional）. Roughness parameters of the 3D surface are also important in analyzing the...The existing surface roughness standards comprise only two dimensions. However, the real roughness of the surface is 3D （three-dimensional）. Roughness parameters of the 3D surface are also important in analyzing the mechanics of contact surfaces. Problems of mechanics of contact surfaces are related to accuracy of 3D surface roughness characteristic. One of the most important factors for 3D characteristics determination is the number of data points per x and y axes. With number of data points we understand its number in cut-off length. Number of data points have substantial influence on the accuracy of measurement results, measuring time and size of output data file （especially along the y-axis direction, where number of data points are number of parallel profiles）. Number of data points must be optimal. Small number of data points lead to incorrect results and increase distribution amplitude, but too large number of data points do not enlarge range of fundamental information, but substantially increase measuring time. Therefore, we must find optimal number of data points per each surface processing method.展开更多
文摘The existing surface roughness standards comprise only two dimensions. However, the real roughness of the surface is 3D （three-dimensional）. Roughness parameters of the 3D surface are also important in analyzing the mechanics of contact surfaces. Problems of mechanics of contact surfaces are related to accuracy of 3D surface roughness characteristic. One of the most important factors for 3D characteristics determination is the number of data points per x and y axes. With number of data points we understand its number in cut-off length. Number of data points have substantial influence on the accuracy of measurement results, measuring time and size of output data file （especially along the y-axis direction, where number of data points are number of parallel profiles）. Number of data points must be optimal. Small number of data points lead to incorrect results and increase distribution amplitude, but too large number of data points do not enlarge range of fundamental information, but substantially increase measuring time. Therefore, we must find optimal number of data points per each surface processing method.
