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<p>Forecasting the Remaining Useful Life (RUL) of cutting tools plays a key role in intelligent predictive maintenance and downtime reduction in today’s manufacturing. Traditional approaches based on hand-designed features or conventional machine learning models are typically incapable of handling intricate temporal relationships and sensor–interaction. This paper proposes a graph-based deep learning pipeline for tool wear forecasting in CNC milling, employing vibration, spindle load, and cutting force signals. Features from both the time-domain (mean, standard deviation, skewness, and kurtosis) and the frequency domain (FFT coefficients, power spectral density) were extracted, and graphs were formed based on correlation and temporal edges in order to model feature dependencies and sequential relationships. Three graph neural network (GNN) architectures were utilized: graph convolutional network (GCN), graph recurrent network (GRN), and graph attention network (GAT). Models were trained and tested on 120 machining runs, and hyperparameter optimization was performed using Optuna and Bayesian Optimization. The experimental results show that Optuna tuning enabled GRN to achieve the strongest time-domain performance (RMSE = 3.68, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\text {R}^2\)</EquationSource> </InlineEquation> = 0.9963), while Bayesian Optimization yielded improved frequency-domain performance for GAT (RMSE = 13.50, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\text {R}^2\)</EquationSource> </InlineEquation> = 0.9487). The designed framework illustrates the power of GNNs for tool-condition monitoring and offers a scalable solution for predictive maintenance, with future work considering physics-informed graph modeling and transfer learning for real-time application.</p>

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Graph neural network-based remaining useful life prediction of milling tools using multi-sensor data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • Satish Kumar,
  • Dhruvi Ramaiya,
  • Sameer Sayyad,
  • Arunkumar Bongale,
  • Vivek Warke,
  • Vaibhav Joshi

摘要

Forecasting the Remaining Useful Life (RUL) of cutting tools plays a key role in intelligent predictive maintenance and downtime reduction in today’s manufacturing. Traditional approaches based on hand-designed features or conventional machine learning models are typically incapable of handling intricate temporal relationships and sensor–interaction. This paper proposes a graph-based deep learning pipeline for tool wear forecasting in CNC milling, employing vibration, spindle load, and cutting force signals. Features from both the time-domain (mean, standard deviation, skewness, and kurtosis) and the frequency domain (FFT coefficients, power spectral density) were extracted, and graphs were formed based on correlation and temporal edges in order to model feature dependencies and sequential relationships. Three graph neural network (GNN) architectures were utilized: graph convolutional network (GCN), graph recurrent network (GRN), and graph attention network (GAT). Models were trained and tested on 120 machining runs, and hyperparameter optimization was performed using Optuna and Bayesian Optimization. The experimental results show that Optuna tuning enabled GRN to achieve the strongest time-domain performance (RMSE = 3.68, \(\text {R}^2\) = 0.9963), while Bayesian Optimization yielded improved frequency-domain performance for GAT (RMSE = 13.50, \(\text {R}^2\) = 0.9487). The designed framework illustrates the power of GNNs for tool-condition monitoring and offers a scalable solution for predictive maintenance, with future work considering physics-informed graph modeling and transfer learning for real-time application.