Fracture mode classification by texture analysis of fracture surface scanning electron microscope images. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Endo, Akihiro; Furuya, Yoshiyuki; Nagata, Kenji; Yoshikawa, Hideki; Shouno, Hayaru
• Is Part Of: Science and Technology of Advanced Materials: Methods. Volume 2:Issue 1(2022)
• Page Start: 129
• Page End: 138
• Publication Date: 2022-12-31
• Subjects: Fractography -- scanning electron microscope -- texture analysis -- machine learning -- classification
• DOI: 10.1080/27660400.2022.2065185
• Languages: English
• ISSNs: 2766-0400

Abstract

ABSTRACT: Fractography is a practical method of determining the cause of a mechanical-structure failure. Accurate decisions regarding fracture-mode classification require experience and knowledge, which may be difficult to share. Therefore, a database of fracture-surface images should be created, and the decision algorithm typically used by experts must be digitized. In recent years, although image classification using deep learning has been successful, it requires a large amount of data and is difficult to interpret. We propose a step-by-step fracture-mode classification method using fracture-surface images, from low to high magnification, based on the fractography knowledge of experts. Fracture-mode classification is performed using texture features for each patch image that is cut out from the fracture-surface image. The fracture mode for the fracture-surface image is voted based on the results of the patch-image classification. In the classification experiments of three fracture modes, the proposed method classifies the fracture mode in patch images with an accuracy of approximately 90%. Moreover, the classification results of the patch images are voted to correctly classify all fracture-surface images as their respective mode, even from a small dataset. Graphical: Figure A1