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Research on the Improvement of Tool Detection Methods Based on Machine Vision

Author(s)

Chaofan Wang, Junwei Tian

Affiliation(s)

School of Mechanical and Electrical Engineering, Xi'an Technological University, Xi'an, Shaanxi, China

Abstract

To address the dual bottlenecks of accuracy and efficiency in traditional tool detection methods, this paper focuses on the research on the improvement of machine vision-based tool detection methods. It aims to overcome the deficiencies of traditional detection methods in accuracy and efficiency, and establish a highly efficient, precise, and automated system for tool parameter detection. The research centers on the optimization of the image edge rough extraction algorithm and the innovation of the sub-pixel edge detection algorithm, and systematically proposes a dynamic weight adaptive sub-pixel edge detection algorithm. Finally, through the methods of feature point recognition and positioning as well as contour fitting, the on-line measurement of tool parameters is realized. According to the experimental results, the system features a high degree of detection automation and fast operation speed, with the measurement accuracy reaching the micron level, which can be effectively applied to the real-time measurement of the geometric parameters of machining tools in industry.

Keywords

Tool Detection; Rough Edge Extraction; Sub-Pixel Edge Detection; Dynamic Weight Adaptation; Online Measurement.

References

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