Fracture mode classification by texture analysis of fracture surface scanning electron microscope images

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.

Keywords

• fractography
• scanning electron microscope
• texture analysis
• machine learning
• classification