ArticleSensors (Basel, Switzerland)2026
Sensor-Derived Mechanism-Informed Prediction of Section-Level Residual Profile Error in Robotic Blade-Edge Finishing.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Robotic belt finishing of turbine-blade edges is difficult to control because local edge radius, contact compliance, and the incoming profile state jointly affect the final residual profile error. This study develops a sensor-derived, mechanism-informed framework for predicting section-level root-mean-square (RMS) residual profile error. Online force measurements, robot and process records, CAD-derived edge geometry, and coordinate measuring machine (CMM) profiles are converted into interpretable section-level descriptors. Three coupled descriptors are introduced to represent the load-to-radius ratio, the force-radius-mismatch interaction, and the normalized radius mismatch. Four Gaussian process regression (GPR) configurations, a training-mean predictor, and a ridge-regression baseline are evaluated using a grouped leave-one-blade-out protocol on eight blades and 80 measured sections. The proposed descriptors show clear predictive value under blade-wise evaluation. Ridge-B3 achieves the best deterministic accuracy, with RMSE = 1.0285 µm and R
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