An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support...

Area of Science:

• Manufacturing Engineering
• Mechanical Engineering
• Data Science

Background:

• Tool wear prediction is crucial for industrial production efficiency.
• Existing machine learning methods often neglect random uncertainty factors in tool wear estimation.
• There is a need for more robust and accurate tool wear prediction techniques.

Purpose of the Study:

• To propose a novel method for predicting cutting tool wear with uncertainty quantification.
• To address the limitations of current machine learning-based tool wear prediction methods.
• To enhance the accuracy and stability of in-advance tool wear predictions.

Main Methods:

• Modeling degradation features using Brownian motion stochastic process.
• Training a Support Vector Regression (SVR) model to map features to tool wear.
• Employing Bayesian inference for online parameter updates and future feature trend estimation.
• Predicting tool wear as distribution densities using simulation samples.

Main Results:

• The proposed method effectively models tool wear degradation with uncertainty.
• In-advance prediction of cutting tool wear was achieved in the form of distribution densities.
• Experimental validation demonstrated superior prediction accuracy and stability compared to existing methods.

Conclusions:

• The novel method offers a significant advancement in cutting tool wear prediction.
• Incorporating stochastic processes and Bayesian inference enhances prediction reliability.
• The approach provides valuable insights for proactive maintenance and optimized manufacturing processes.