{"id":3640,"date":"2026-04-12T22:05:35","date_gmt":"2026-04-12T14:05:35","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=3640"},"modified":"2026-06-01T20:28:37","modified_gmt":"2026-06-01T12:28:37","slug":"deep-learning-driven-adaptive-machining-parameter-optimization-for-high-precision-cnc-milling","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=deep-learning-driven-adaptive-machining-parameter-optimization-for-high-precision-cnc-milling","title":{"rendered":"Deep Learning-Driven Adaptive Machining Parameter Optimization for High-Precision CNC Milling"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 31<\/a><\/p>\n<\/div>\n\n\n\n

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Xiaoli Qu<\/i><\/a><\/p>\n\n\n\n

Zhengzhou Technical College, No. 081, Zhengshang Road, Zhengzhou City, China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0January 21, 2026
Accepted:\u00a0March 9, 2026
Publication Date:\u00a0April 12, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Overall architecture of the proposed framework<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| https:\/\/doi.org\/10.6180\/jase.202608_31.067<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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 GNNto 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%\u22128.9% in accuracy. This framework provides a reliable solution for intelligent predictive maintenance in CNC turning processes, balancing diagnostic accuracy and real-time performance.<\/p>\n\n\n\n

Keywords:\u00a0Deep Learning-Driven; Adaptive Machining; Parameter Optimization; High-Precision CNC Milling<\/em><\/p>\n\n\n\n

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