An image processing-based crack detection technique for pressed panel products. (October 2020)

• Record Type: Journal Article
• Authors: Miao, Yinan; Jeon, Jun Young; Park, Gyuhae
• Is Part Of: Journal of manufacturing systems. Volume 57(2020)
• Page Start: 287
• Page End: 297
• Publication Date: 2020-10
• Subjects: Crack detection -- Image processing -- Computer vision -- Percolation process -- Metal crack; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.10.004
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A crack detection system is proposed for fast quality monitoring of pressed panel products on the highly automated production line. Pre-stored baseline images are not required in the proposed method, which improves the applicability for real manufacturing lines. Computational cost can be significantly reduced without much loss of the accuracy by performing the two-stage detection. The performance of the proposed technique is further improved by light control and percolation-based shape recognition. Abstract: Crack detection is an important step in assessing the quality of pressed panel products. This paper presents a fast and non-invasive crack detection technique which involves extracting the outline of the captured object and applying a unique edge line evaluation. This technique is robust against environmental condition changes and only require a low-cost web camera. After capturing an image immediately following the press process, a clear one-pixel edge line is extracted by applying a light control and a series of pre-image processing algorithms, including a valley-emphasis Otsu method and percolation-based shape recognition. Next, the initial detection at low resolution is applied to search for every possible crack using unique edge line and curvature evaluation. Finally, at high resolution, the windowed image of every possible crack is individually analyzed to detect existing cracks using a more specific evaluation process. All of these steps are completed within 0.5 s, thus allowing for the technique to be applied in real-time on a highly automated manufacturing line. To demonstrate the performance of the proposed technique, experiments are conducted on an aluminum plate with different patterns and the pressed panel products. The results show that the proposed technique can detect surface cracks on pressed panels with stable performance as well as high accuracy and efficiency.