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Image edge detection based tool condition monitoring with morphological component analysis
Xiaolong Yu1, Xin Lin1, Yiquan Dai2
1Department of Automation, University of Science and Technology of China, Hefei 230026, China; Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Huihong Building, Changwu Middle Road 801, Changzhou 213164, Jiangsu, China.
ISA Transactions
|April 10, 2017
Summary
This study introduces a new tool wear monitoring method using image edge detection. It accurately monitors tool conditions for enhanced precision in automated manufacturing.
Area of Science:
- Manufacturing Engineering
- Computer Vision
- Materials Science
Background:
- Tool condition monitoring is crucial for precision in automated manufacturing.
- Existing methods for edge detection can be affected by noise and texture.
- Accurate characterization of tool wear is essential for process control.
Purpose of the Study:
- To propose a novel approach for tool wear monitoring using image edge detection.
- To improve the accuracy and integrity of tool edge extraction.
- To reduce the influence of noise and texture in wear measurement.
Main Methods:
- Utilized morphological component analysis for tool edge extraction.
- Employed sparse representation for target image analysis.
- Integrated edge detection with image decomposition techniques.
Main Results:
- Successfully extracted continuous and complete tool wear edges.
- Demonstrated improved integrity and connectivity of detected edges compared to existing algorithms.
- Achieved better geometric accuracy and a lower error rate in tool condition estimation.
Conclusions:
- The proposed approach offers a robust method for tool wear monitoring.
- It enhances the reliability and accuracy of tool condition assessment in automated manufacturing.
- This method provides a convenient way to characterize tool conditions for improved precision.