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Physics-data fusion multi-modal deep learning for spindle current prediction in digital twin-driven machining state perception

Year: 2026

Type: article

Abstract: Accurate perception of machining states is essential for intelligent Computer Numerical Control (CNC) systems, yet modeling key process variables remains challenging due to the nonlinear and multi-modal nature of cutting processes. Empirical models lack adaptability, while purely data-driven methods often neglect physical constraints. Direct measurement of cutting force is challenging in practice, as complex workpiece geometries, limited ranges of detection instruments, and high deployment costs make large-scale force data acquisition difficult. Therefore, spindle current is adopted as a more practical and accessible variable for state perception. In consideration of this problem, this study proposes a physics–data fusion multi-modal deep learning framework for spindle current prediction in a Digital Twin (DT)-driven environment. The framework integrates multi-modal inputs, including acceleration signals, STEP-NC semantic metadata, and cutter-workpiece engagement (CWE) geometric descriptors derived from the 3D engagement volume, into a unified representation that is processed by a data-driven branch for feature extraction. The architecture features a Sequence Head for residual regression and an attention-pooled Point Head for next-step prediction. Spindle dynamics constrain sequence outputs through a physics-informed module. Conditioned on process embeddings, a multi-objective loss adaptively balances dual heads and physical heuristics. The framework, deployed in a DT environment, supports synchronized multi-source data acquisition and real-time prediction. Experimental validation on a dedicated DT platform confirms framework feasibility, consistently maintaining R 2 > 0.95 and superior error metrics across diverse machining regimes.

Source: Journal of Manufacturing Systems

DOI: 10.1016/j.jmsy.2026.09.022