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Tool Condition Monitoring for High-Performance Machining Systems-A Review
Ayman Mohamed1, Mahmoud Hassan2, Rachid M'Saoubi3
1Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0C3, Canada.
Sensors (Basel, Switzerland)
|March 26, 2022
Summary
Tool condition monitoring (TCM) systems are crucial for Industry 4.0 manufacturing. This review covers TCM components, wireless sensors, and data processing for enhanced automated machining.
Area of Science:
- Manufacturing Engineering
- Automation and Control Systems
- Materials Science
Background:
- Industry 4.0 drives demand for automated, high-precision manufacturing.
- Tool Condition Monitoring (TCM) is vital for optimizing machining efficiency and part quality.
- Unmanned systems require robust monitoring of tool health (wear, breakage).
Purpose of the Study:
- To review state-of-the-art Tool Condition Monitoring (TCM) systems.
- To analyze wireless tool-embedded sensor nodes for Industrial Internet of Things (IIOT) readiness.
- To provide guidance on dimensionality reduction techniques for TCM data.
Main Methods:
- Literature review of TCM system components: sensing, data acquisition, signal processing, and models.
- Analysis of wireless sensor node development, advantages, and limitations.
- Comprehensive review and comparison of dimensionality reduction techniques.
Main Results:
- Identified key components and current practices in TCM systems.
- Highlighted challenges and benefits of wireless tool-embedded sensors for IIOT integration.
- Assessed various dimensionality reduction methods for TCM data processing.
Conclusions:
- TCM systems are essential for advanced manufacturing and Industry 4.0.
- Wireless sensor nodes and effective data processing are critical for TCM generalization and reliability.
- Future research should focus on improving TCM accuracy, robustness, and integration.
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