Intelligent automation aiding rapid surface feature quantification in 3D

Automatic small surface feature inspection of high-precision components requires superior measurement and automated analysis systems. Early identification and quantification (depth, area and volume) is a key aspect in quality assurance in order to check the health of the manufactured part. Human visual analysis of surface feature inspection is qualitative, subjective and time consuming. Three-dimensional (3D) robotic inspection should provide a more robust and systematic quantitative approach for surface defect measurement. 3D measuring instruments typically generate point cloud data as an output, although via different principles. Data processing of point cloud data is often subject to repeatability issues causing significant concern with data confidence. This research is concerned with the measurement of novel traceable sub-millimetric surface defects and the development of a novel, robust, repeatable and mathematical solution for automatic defect detection and quantification. This is then extended to a surface defect on a plain bearing measured in real-time using a 3D measuring instrument mounted on a 6-axis robot and quantified using the novel algorithm. The results show that the new surface defect measurement and quantification is more robust, efficient, and repeatable than existing solutions.