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Development of Deep Belief Network for Tool Faults Recognition.

Archana P Kale¹, Revati M Wahul¹, Abhishek D Patange²

¹Department of Computer Engineering, Modern Education Society's College of Engineering (MESCOE), Pune 411001, India.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This study introduces a Deep Belief Network (DBN) to identify tool failure in milling operations. The model accurately classifies six tool conditions using real-time vibration signals, enhancing manufacturing quality control.

Keywords:

deep belief network face milling fault diagnosis tool faults recognition

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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.