Minimizing optical measurement time of micro spur gears through point cloud completion techniques

In micro gear manufacturing, quality assessment relies on full-geometry measurements to evaluate functional performance. Due to tolerances down to one micrometer, high-end metrology is essential. Optical systems enable fast, non-contact measurements and can, in principle, be used for full-gear scans. These scans serve as input for single-flank rolling simulations, which assess how geometric deviations affect functional behavior such as transmission accuracy. However, full scans remain time-consuming and often unsuitable for inline inspection due to the trade-off between speed and measurement uncertainty. We address this by proposing a partial-scan workflow, based on the observation that tool-induced deviations propagate periodically across gear teeth. This allows reconstruction of micrometer-accurate point clouds from a subset of teeth. We compare a deep learning-based completion network with an analytical reconstruction, evaluating both geometrically and functionally. While the deep learning approach shows higher geometric fidelity, it falls short in functional accuracy. This reveals a common gap in learning-based methods, where achieving geometric similarity may fail to preserve the underlying functional behavior. Our approach enables faster inspection while maintaining confidence in gear performance.