Tool marks on face-milled surfaces contain huge information about the manufacturing processes. Direct surface segmentation based on tool marks is in favor of surface error sources diagnoses. However, traditional surface segmentation methods are prone to over-segmentation when partition surfaces with tool marks. This paper proposes an improved segmentation approach to solve this problem. Based on surface topography measured by high definition metrology (HDM), the surface segmentation methodology mainly involves four steps: automatic subsurface selection, local thresh-holding, broken tool marks repairing and water segmentation (abbreviated as “STRW” methodology). A novel concept called “periodic degree” is proposed and used as the criteria of subsurface selection. A binary image of tool marks is created by an adaptive local threshold. Broken tool marks are identified by a distance threshold and repaired by a convex-hull based tool marks repairing algorithm. Finally, water segmentation is applied to divide the surface into different regions and each region belongs to a unique tool tooth trajectory. Three real cases from powertrain plant demonstrate the procedures of the methodology and verify the effectiveness of the proposed methodology.

端面铣削表面上的工具标记包含有关制造过程的大量信息。基于工具标记的直接表面分割有利于表面误差源诊断。然而，当用工具标记来分割表面时，传统的表面分割方法易于过度分割。本文提出了一种改进的分割方法来解决这个问题。基于通过高清晰度计量学（HDM）测量的表面形貌，表面分割方法主要包括四个步骤：自动地下选择，局部脱粒，工具痕迹修复和水分割（简称为“ STRW”方法）。提出了一种称为“周期度”的新颖概念，并将其用作地下选择的标准。工具标记的二进制图像由自适应局部阈值创建。破损的工具标记通过距离阈值进行标识，并通过基于凸包的工具标记修复算法进行修复。最后，应用水分割将表面划分为不同的区域，并且每个区域都属于唯一的工具齿轨迹。动力总成工厂的三个实际案例演示了该方法的程序，并验证了所提出方法的有效性。