An Image-Based Hairline Crack Identification Method for Metal Parts

Accurate detection and measurement of hairline cracks on metal parts, typically less than 1mm wide, are important for remanufacturing. However, this task remains challenging due to the small crack size, high reflectance of the metal surface, and the lack of reliable real-size measurement methods. This paper proposes an image-based identification method that addresses these challenges and enables precise detection and measurement of hairline cracks on metal surfaces. This method establishes a controllable imaging system with a blue dome light source to capture uniformly illuminated and high-contrast images. This setup overcomes the primary difficulty of high reflectance associated with detecting cracks on metal surfaces. Furthermore, the imaging system incorporates camera calibration to convert pixel size to real size accurately, offering significant practical value beyond mere crack localization and segmentation at the pixel level. A new hairline crack identification algorithm based on morphology is developed to extract pixels of the hairline cracks directly, followed by real-size calculation. Experimental results validate the proposed method's effectiveness, achieving an accuracy of approximately 97% and 90% for millimeter-level and submillimeter-level hairline cracks, respectively. Moreover, the average error and standard deviation surpass those of previous crack measurement techniques, demonstrating the proposed method's superiority.