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The tool condition plays a critical role in CNC machining processes and requires rigorous monitoring to ensure machining quality and minimize production costs. This paper presents a triaxial fusion network for tool wear prediction using Gramian Angular Summation Field (GASF) encoded force signals. The method transforms one-dimensional cutting forces (Fx, Fy, Fz) into two-dimensional images via GASF encoding, enabling the use of convolutional neural networks while preserving temporal correlations. In this way, specialized features from each force component can be extracted and fused for efficient prediction of three wear states: break-in, steady-state, and severe wear. Experiments on the PHM 2010 dataset with EfficientNet-B0 backbone for triaxial components demonstrate superior performance, achieving prediction accuracies of 90.48% on C1, 95.56% on C4, and 91.11% on C6, with macro-averaged F1-scores of 90.78%, 95.70%, and 91.26%, respectively. The results validate the effectiveness of the triaxial fusion approach for real-time CNC tool wear monitoring applications.

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Triaxial Fusion Network for Tool Wear Prediction Using GASF-Encoded Force Signals

  • 会议论文

  • 发表日期：

    2026年

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  • Hau Than Diep,
  • Thanh Huy Phung,
  • Van Xuan Tran,
  • Quoc Chi Nguyen

摘要

The tool condition plays a critical role in CNC machining processes and requires rigorous monitoring to ensure machining quality and minimize production costs. This paper presents a triaxial fusion network for tool wear prediction using Gramian Angular Summation Field (GASF) encoded force signals. The method transforms one-dimensional cutting forces (Fx, Fy, Fz) into two-dimensional images via GASF encoding, enabling the use of convolutional neural networks while preserving temporal correlations. In this way, specialized features from each force component can be extracted and fused for efficient prediction of three wear states: break-in, steady-state, and severe wear. Experiments on the PHM 2010 dataset with EfficientNet-B0 backbone for triaxial components demonstrate superior performance, achieving prediction accuracies of 90.48% on C1, 95.56% on C4, and 91.11% on C6, with macro-averaged F1-scores of 90.78%, 95.70%, and 91.26%, respectively. The results validate the effectiveness of the triaxial fusion approach for real-time CNC tool wear monitoring applications.