A Study on the Detection of Hole in Automotive CV Joint Boot Using Image Processing and AI Techniques

Detecting and analyzing defects in components or systems is crucial for maintaining high-quality standards in modern manufacturing and quality control. Recently, imaging-based defect detection methods have gained popularity across various engineering fields, highlighting their growing importance. Additionally, the integration of Artificial Intelligence (AI) to improve accuracy and efficiency is rapidly advancing. This paper presents a system that uses imaging to detect holes in CV joint boots, as these holes significantly affect the overall performance and durability of the system. Moreover, it introduces a method for enhancing detection performance by applying AI techniques. Validation tests on actual CV joint boots confirmed that the proposed method improves detection performance.

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Publication Details

Journal
Journal of the Korean Society for Precision Engineering
Published
2025-10-27
DOI
https://doi.org/10.7736/jkspe.025.050
Primary Topic
Engineering Applied Research
Type
article
Field-Weighted Citation Impact
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article

A Study on the Detection of Hole in Automotive CV Joint Boot Using Image Processing and AI Techniques

Hyeongill Lee, Y.H. Lim
Journal of the Korean Society for Precision Engineering
Engineering Applied Research
article

A Study on the Detection of Hole in Automotive CV Joint Boot Using Image Processing and AI Techniques

Hyeongill Lee, Y.H. Lim
article en

Abstract

Detecting and analyzing defects in components or systems is crucial for maintaining high-quality standards in modern manufacturing and quality control. Recently, imaging-based defect detection methods have gained popularity across various engineering fields, highlighting their growing importance. Additionally, the integration of Artificial Intelligence (AI) to improve accuracy and efficiency is rapidly advancing. This paper presents a system that uses imaging to detect holes in CV joint boots, as these holes significantly affect the overall performance and durability of the system. Moreover, it introduces a method for enhancing detection performance by applying AI techniques. Validation tests on actual CV joint boots confirmed that the proposed method improves detection performance.

Journal of the Korean Society for Precision EngineeringVol. 42(10)
Openalex Percentile: Top 68%
Engineering Applied Research
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