A virtual measurement method for evaluating the gear radial composite deviation based on point cloud data

Abstract

Radial composite deviation is an important index to evaluate the precision of gear transmission, and its detection method is also an important trend of gear detection in industrial development. The ability to predict radial composite deviation while obtaining geometric deviation of gears is a key challenge in advancing intelligent online inspection within gear manufacturing workshops. This study presents a virtual measurement method for determining gear radial composite deviation using point cloud data. The method involves using an optical three-dimensional scanner to capture tooth surface cloud data, determining tooth profile deviation, and applying reverse engineering technology to reconstruct the gear model, the variation curve of gear center distance is determined by simulating, allowing for the calculation of the total radial composite deviation and tooth-to-tooth radial composite deviation. In comparison to actual measurement, the virtual measurement method is generally reliable. It can effectively illustrate the overall trend of center distance variation and has good interpretability for predicting radial composite deviation results. This method reduces the dependence on the specialized gear measuring instrument and addresses the critical issue of simultaneously obtaining tooth profile deviation and radial composite deviation.

Keywords

• Gear measurement
• Double-flank rolling test
• Virtual measurement
• Point cloud data
• Reverse engineering