Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time-Frequency-Based Features and Deep Learning
Sameer Sayyad1, Satish Kumar1,2, Arunkumar Bongale1
1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India.
Sensors (Basel, Switzerland)
|July 8, 2023
Summary
Predicting the remaining useful life (RUL) of milling cutters is crucial for manufacturing efficiency. Time-frequency domain features combined with deep learning models like LSTM and hybrid approaches significantly improve RUL prediction accuracy.
Area of Science:
- Manufacturing Engineering
- Artificial Intelligence
- Machine Learning
Background:
- Milling machines are vital in manufacturing due to their versatility.
- Cutting tool accuracy and surface finishing directly impact industrial productivity.
- Monitoring cutting tool life is essential to prevent downtime from tool wear.
Purpose of the Study:
- To accurately predict the remaining useful life (RUL) of milling cutters.
- To enhance machining accuracy and surface finishing by preventing unplanned downtime.
- To optimize the utilization of cutting tool life in milling operations.
Main Methods:
- Utilized the IEEE NUAA Ideahouse dataset for RUL estimation.
- Employed time-frequency domain (TFD) feature extraction techniques, including short-time Fourier-transform (STFT) and wavelet transforms (WT).
- Applied deep learning (DL) models such as Long Short-Term Memory (LSTM) variants, Convolutional Neural Networks (CNN), and hybrid CNN-LSTM models.
Main Results:
- Feature engineering quality is critical for accurate RUL prediction.
- TFD features combined with LSTM variants and hybrid models demonstrated strong performance.
- The proposed methods achieved improved prediction accuracy for milling cutting tool RUL.
Conclusions:
- Accurate RUL prediction is essential for maximizing cutting tool life and industrial productivity.
- TFD feature extraction coupled with advanced DL models offers a promising approach for milling tool RUL estimation.
- This research contributes to reducing machining downtime and improving overall manufacturing efficiency.
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