Xiaoli QuThis email address is being protected from spambots. You need JavaScript enabled to view it.
Zhengzhou Technical College, No. 081, Zhengshang Road, Zhengzhou City, China
Received: January 21, 2026 Accepted: March 9, 2026 Publication Date: March 27, 2026
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
To address the limitations of single-modal data and low real-time performance in traditional anomaly diagnosis for CNC turning processes, this paper proposes a novel framework integrating adaptive multi-modal data fusion and lightweight graph neural network (GNN) for real-time anomaly diagnosis. First, multi-modal data (vibration, spindle current, and cutting force) are collected and preprocessed to extract time-frequency domain features. A mutual information-based graph construction method is designed to model the intrinsic correlations between multi-modal features, converting non-Euclidean feature data into structured graph data. Then, an event-driven lightweight GNN (EL-GNN) is proposed, which adopts a hierarchical propagation mechanism to reduce redundant computations and realizes millisecond-level inference. A cross-attention fusion module is embedded in the GNN to dynamically assign weights to different modal features, enhancing the robustness to noise. Experiments are conducted on a self-built CNC turning test platform and the public tool wear dataset. Results show that the proposed framework achieves an anomaly diagnosis accuracy of 98.73%, a recall rate of 98.51%, and a P99 inference latency of 28.3 ms , outperforming traditional machine learning methods and deep learning models by 3.2%−8.9% in accuracy. This framework provides a reliable solution for intelligent predictive maintenance in CNC turning processes, balancing diagnostic accuracy and real-time performance.
Keywords: Deep Learning-Driven; Adaptive Machining; Parameter Optimization; High-Precision CNC Milling
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