Journal
ENGINEERING FAILURE ANALYSIS
Volume 59, Issue -, Pages 237-252Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engfailanal.2015.10.008
Keywords
Ductile sudden fracture; Brittle sudden fracture; Progressive fracture due to fatigue; Artificial Neural Network; Support Vector Machine
Ask authors/readers for more resources
The first step in the failure analysis is based on the visual inspection of the fracture surface. This is the base for the development of any fractographic process and it represents the main means for fracture classification. In several occasions, this process is carried out by non-suitable personnel in this area, which increases the chances of generating a wrong classification and, therefore, of negatively altering the results of the entire process. By using artificial vision techniques, this document shows the work done for classifying three types of fractures: brittle sudden, ductile sudden, and progressive due to fatigue, all these in metallic materials in order to promote failure analysis on a fracture surface. The employed descriptors: Haralick's features, energy masks, and the fractal dimension (41, overall), were generated from the Gray Level Co-occurrence Matrix (GLCM), the texture energy laws, and the fractal analysis (respectively) applied to obtained full-scale images, different from other studies using SEM for the acquisition of the data set. The Artificial Neural Networks classifiers and the Support Vector Machine performance were analyzed, finding out that the first one obtained the best results. Such results can be compared to the ones obtained by an expert in this field, with an accuracy percentage higher than 80% for the three types of fractures. (C) 2015 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available