ArticleSensors (Basel, Switzerland)2025
XGBoost-Based Digital Twin Model for Predicting Trajectory Errors in a Hexapod Coordinated Machining System Using Positioning Accuracy and Vibration Data.
Article in Sensors (Basel, Switzerland), 2025. 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
Dynamic errors in robotic machining can degrade part quality, particularly in flexible platforms that are susceptible to both geometric and inertial disturbances. This work introduces a data-driven digital twin for pointwise prediction of circular trajectory errors in a hexapod-based machining cell, using a compact sensing configuration that combines ballbar measurements with tri-axial vibration signals. Deviations measured by ballbar, acceleration data, and CMM-measured profiles are synchronized in the angular domain via a unified pipeline for denoising, resampling, and phase alignment. Sliding-window vibration statistics and the ballbar path error are used as inputs to XGBoost, multilayer perceptron, and random forest regressors. Model performance is evaluated under a deployment-relevant leave-one-run-out protocol and a conventional random 70:30 point split. XGBoost achieves micrometer-level accuracy on unseen runs, with RMSE around 5 µm, R
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