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This study introduces a data processing pipeline for predicting milling machine tool condition using real-time machine learning. A novel kernel function significantly improves prediction accuracy, especially for lightly worn tools.

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Area of Science:

  • Manufacturing Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Real-time machine learning for manufacturing machine monitoring is advancing.
  • Accurate prediction from raw sensor data remains a challenge.

Purpose of the Study:

  • Develop a data processing pipeline to predict milling machine tool condition.
  • Improve prediction accuracy using novel signal processing techniques.

Main Methods:

  • Aggregated acceleration and audio time series sensor data into blocks for cutting operations.
  • Preprocessed data using efficient signal processing techniques.
  • Proposed a novel kernel function for Gaussian process regression models.

Main Results:

  • The novel kernel function outperformed common covariance functions in predicting tool condition.
  • The developed model accurately predicts the condition of lightly worn tools.
  • Models were represented using Predictive Model Markup Language (PMML) for standardization.

Conclusions:

  • The proposed data processing pipeline and novel kernel function enhance the accuracy of manufacturing machine tool condition monitoring.
  • Standardized model representation facilitates integration into existing systems.