Segmentation of workpiece surfaces with tool marks based on high definition metrology

Abstract

Abstract 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.