The cellulose acetate replication technique is an important method for studying material fatigue. However, extracting accurate information from pictures of cellulose replicas poses challenges because of distortions and numerous artifacts. This paper presents an image processing procedure for effective fatigue crack identification in plastic replicas. The approach employs thresholding, adaptive Gaussian thresholding, and Otsu binarization to convert gray-scale images into binary ones, enhancing crack visibility. Morphological operations refine object shapes, and Connected Components Analysis facilitates crack identification. Despite limited data, the handcrafted feature extraction algorithm proves robust, addressing challenges. The algorithm shows efficacy in detecting cracks as small as 30 μm, even in the presence of cellulose replication artifacts. The results highlight ability to capture significant cracks’ orientation, length, and growth stages, essential for understanding fatigue mechanisms. Analysis of results, especially evaluation metrics encompassing false positives and false negatives, provides a comprehensive understanding of the algorithm’s strengths and limitations. The proposed tool is available on GitHub.
Authors
- Krzysztof Pałczyński,
- Jan Seyda,
- Dariusz Skibicki,
- Łukasz Pejkowski,
- dr hab. inż. Wojciech Macek link open in new tab
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1016/j.engfailanal.2024.108663
- Category
- Publikacja w czasopiśmie
- Type
- artykuły w czasopismach
- Language
- angielski
- Publication year
- 2024
