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Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion.

Kang Wang1, Aimin Wang1, Long Wu2

  • 1Digital Manufacturing Institute, Beijing Institute of Technology, Beijing 100081, China.

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|April 27, 2024
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Summary

This study introduces a new tool wear prediction method using multi-sensor data fusion and a ResNet-LSTM model. The advanced technique accurately forecasts tool conditions, improving manufacturing efficiency and preventing unexpected tool breakages.

Keywords:
LSTM networkdeep residual networkmulti-sensor information fusiontool wear prediction

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Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Intelligent monitoring of cutting tools is crucial for manufacturing efficiency.
  • Accurate prediction of tool wear and breakage is essential to prevent downtime and ensure part quality.

Purpose of the Study:

  • To propose a novel tool wear prediction technique using multi-sensor information fusion.
  • To develop an accurate predictive model for tool wear by integrating ResNet and LSTM.
  • To enhance the capability of signal feature extraction and noise reduction.

Main Methods:

  • Collected vibrational, current, and cutting force signals during machining.
  • Extracted features from sensor signals.
  • Applied Kalman filtering for feature fusion.
  • Developed a hybrid ResNet-LSTM model for tool wear prediction.

Main Results:

  • The ResNet-LSTM model significantly improved prediction accuracy compared to individual ResNet and LSTM models.
  • Demonstrated adaptive noise reduction capabilities during signal feature extraction.
  • Achieved an average prediction error of 0.0085 mm and 98.25% prediction accuracy.

Conclusions:

  • The proposed ResNet-LSTM model effectively predicts tool wear using multi-sensor fusion.
  • The method enhances signal feature extraction and noise reduction, validating its feasibility for intelligent tool monitoring.