Classification of Welding Defects Using Gray Level Histogram Techniques via Neural Network. | ||||
| MEJ- Mansoura Engineering Journal | ||||
| Article 11, Volume 39, Issue 4, December 2014, Page 1-13 PDF (908.38 K) | ||||
| Document Type: Research Studies | ||||
| DOI: 10.21608/bfemu.2020.102839 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Wael Khalifa  ; Ossama Abouelatta ; Elamir Gadelmawla; Ibrahim Elewa
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| Production Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, 35516 Mansoura, Egypt | ||||
| Abstract | ||||
| Technological development accompanied the need to get a high-quality welding. The important industries such as oil and auto industries and other important industries need to rely on reliable welding operations; collapse as a result of this welding may mean a great loss in lives and money. This paper aimed to produce an automatic system to detect, recognize and classify welding cases (defects and no defects) in radiography images was described depending upon image histogram technique. Two main steps to do that, In the first step, image processing techniques, including converting color images to gray scale, filtering image, and resizing were implemented to help in the image array of weld images and the detection of weld defects. The second step, a proposed program was build in-house depending upon Matlab to classify and recognize automatically six types of weld defects met in practice, it is Porosity – Undercut – Lac of fusion – Crack – Slag –Cavity, plus the non-defect type. It was clear from the results that it can rely on this method significantly, reaching rates as well as the appointment of defects and no defects to about 94.3%. | ||||
| Keywords | ||||
| Welding defects; Neural network; Computer vision; X; ray; Image histogram | ||||
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