Development of Deep Belief Network for Tool Faults Recognition

Area of Science:

• Manufacturing Engineering
• Artificial Intelligence
• Machine Learning

Background:

• Precise machining outcomes depend on controlled interactions between work material and cutting tools.
• Deviations in cutting parameters (speed, feed, depth of cut) lead to tool deterioration and affect workpiece quality.
• Intelligent techniques are crucial for recognizing and describing tool failure, with deep learning showing promise for dynamic data analysis.

Purpose of the Study:

• To develop a Deep Belief Network (DBN) for on-board pattern recognition of tool conditions in milling operations.
• To classify six distinct tool conditions, including healthy and five types of faulty states.
• To explore descriptive analytics for tool condition monitoring beyond mere prediction.

Main Methods:

• Development and implementation of a Deep Belief Network (DBN) model.
• Acquisition of real-time, image-based vibration signals during milling operations.
• Training, testing, and validation of the DBN model using diverse datasets with varied input parameters.

Main Results:

• The DBN model successfully classified six different tool conditions (one healthy, five faulty).
• The system demonstrated effectiveness in recognizing variations leading to tool faults through vibration signal analysis.
• The approach provides a foundation for descriptive analytics in real-time tool condition monitoring.

Conclusions:

• Deep Belief Networks are effective for classifying tool conditions in real-time milling based on vibration signals.
• The developed model contributes to intelligent techniques for recognizing and describing tool failure in manufacturing.
• This research highlights the potential of deep learning for descriptive analytics in industrial applications.