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A holistic approach for improving milling machine cutting tool wear prediction

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Abstract

Predictive maintenance (PdM) has tremendous potential for reducing total operational costs and turnaround time in industrial manufacturing and maintenance services. In recent years, many deep learning based prediction methods have emerged and advanced the state-of-the-art in the PdM area. This paper proposes a holistic tool wear prediction framework that orchestrates convolutional neural networks for multisensor sequence learning, synthetic features to augment small training datasets, and a meticulously designed training regulation strategy. The effectiveness of the proposed framework is evaluated with computer numerical control (CNC) machine milling datasets available in the public domain. The results show that our method, sCNN-Ex, outperforms state-of-the-art tool wear prediction methods. sCNN-Ex reduces the prediction mean absolute percentage error from existing 32% to 21.7% for steel cases in the NASA milling dataset.

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Notes

  1. RMSE and MAPE values in Table 2 are averages over all test cases by material.

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  1. ST Engineering IHQ, Ang Mo Kio, Singapore

    Yeli Feng

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  1. Yeli Feng
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Correspondence to Yeli Feng.

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Yeli Feng is a principal research scientist with ST Engineering IHQ Pte. Ltd..

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Feng, Y. A holistic approach for improving milling machine cutting tool wear prediction. Appl Intell 53, 30329–30342 (2023). https://doi.org/10.1007/s10489-023-04793-0

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