Publication: Weld defect classification using polar radius signature and neural network
| datacite.subject.fos | oecd::Engineering and technology::Mechanical engineering | |
| dc.contributor.author | Teow, Soo Pei | |
| dc.date.accessioned | 2024-08-06T09:27:44Z | |
| dc.date.available | 2024-08-06T09:27:44Z | |
| dc.date.issued | 2005-03-01 | |
| dc.description.abstract | An automatic defect classification system was developed in this research using simulated image database and polar radius signature and neural network classifier to identify different types of defect in radiographic images of welds. Programs were developed by using Languange C to obtain polar radius signature and roughness parameters from simulated images for subsequent classification. The image processes involved are blob analysis, binarization, edge detection, etc to extract polar radius signature. Several roughness parameters such as Ra, Rq, Rz, Rp and Rv were then extracted from the polar radius signature from the simulated images. Neural network was employed to train the simulated data. A total of 4 defect types were studied and the classification was carried out using several different combinations of roughness parameters and types of weld defect. The highest accuracy of 81.25% was achieved in classifying crack and incomplete penetration by using five parameters. Therefore, roughness parameters which are extracted from polar radius signature have potential in weld defect classification. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/20060 | |
| dc.language.iso | en | |
| dc.title | Weld defect classification using polar radius signature and neural network | |
| dc.type | Resource Types::text::report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |