The on-machine measurement (OMM) of aero-engine blades is a critical technology for enabling closed-loop manufacturing. However, when using line laser sensors with a fixed scanning pose to measure free-form surfaces, the variation in surface geometry leads to changing incident angles, which in turn induce non-stationary noise. To address this issue, this paper proposes a multi-sensor fusion method utilizing Adaptive Heteroscedastic Gaussian Process Regression (AHGPR) based on a Spatial-Angle-Balanced Sampling (S-ABS) strategy. The AHGPR explicitly integrates the physical mapping of incident angle errors into its covariance structure, thereby automatically adjusting observation weights according to the local geometric posture. Concurrently, the S-ABS strategy captures the high-error characteristic points with large incident angles while maintaining a globally uniform spatial distribution. The experimental data indicate that this approach addresses the sampling deficiency encountered at the leading and trailing edges and in areas with large incident angles. The proposed approach reduced the impact of optical deviations on measurement accuracy and improved the precision of the process.
Zheng et al. (Sat,) studied this question.