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March 7, 2026SHILAP Revista de lepidopterologíaOpen Access

Multi-Modal Data Fusion and Graph Neural Network for Real-Time Anomaly Diagnosis in CNC Turning Processes

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Authors

XQXiaoli Qu

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Overview

Proposes an adaptive framework for optimizing CNC milling processes, suggesting improvements in efficiency and precision.

Key Points

  • The aim is to create a deep learning framework for optimizing CNC milling parameters under changing conditions.
  • Developed a hybrid CNN-LSTM model combined with Bayesian optimization for key output prediction.
  • Extracted features from multi-sensor signals including vibration, temperature, and current.
  • Constructed a multi-objective optimization model focused on precision, efficiency, and energy use.
  • Implemented an improved particle swarm optimization algorithm with real-time feedback for optimization.
  • Conducted experiments with aluminum and titanium alloys using Taguchi and extended full factorial designs.
  • CNN-LSTM-BO model shows improved prediction accuracy over single CNN, LSTM, and traditional regression models.
  • Reduced surface roughness (Ra) by 18.7% to 23.5%.
  • Increased material removal rate (MRR) by 12.3% to 16.8%.
  • Lowered specific energy consumption by 10.2% to 14.6% compared to non-adaptive methods.

Cite This Study

Xiaoli Qu (2026) studied this question.

synapsesocial.com/papers/69abc1a65af8044f7a4ea6ffhttps://doi.org/10.6180/jase.202608_31.032
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Deep Learning-Driven Adaptive Machining Parameter Optimization for High-Precision CNC Milling2026
  2. 2Hybrid Deep Learning And Metaheuristic Optimization For Cnc Machining Parameter Prediction2026
  3. 3A Multimodal Machine Learning Framework for Optimizing Coated Cutting Tool Performance in CNC Turning Operations2026
  4. 4Optimization of Machining Parameters in CNC Turning Using Statistical and Machine Learning Approaches2026
  5. 5Optimization of Machining Parameters in CNC Turning Using Statistical and Machine Learning Approaches2026