TY - JOUR

T1 - Minimizing optical measurement time of micro spur gears through point cloud completion techniques

AU - Bilen, Ali

AU - Decman, Max

AU - Stamer, Florian

AU - Lanza, Gisela

PY - 2025/12/31

Y1 - 2025/12/31

N2 - 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.

AB - 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.

KW - Focus variation

KW - Measurement time optimization

KW - Micro gears

KW - Point cloud completion

KW - Engineering

UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=leuphana_woslite&SrcAuth=WosAPI&KeyUT=WOS:001644555400001&DestLinkType=FullRecord&DestApp=WOS_CPL

U2 - 10.1088/1361-6501/ae1d9c

DO - 10.1088/1361-6501/ae1d9c

M3 - Journal articles

VL - 36

JO - Measurement Science and Technology

JF - Measurement Science and Technology

SN - 0957-0233

IS - 12

M1 - 125202

ER -