Characterization of Welding Discontinuities by Combined Phased Array Ultrasonic and Artificial Neural Network | ||||
| Engineering Research Journal | ||||
| Article 16, Volume 163, Issue 0, September 2019, Page 257-276 PDF (1.77 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/erj.2019.122537 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| ahmed mazloum* 1; ahmed mahrous1; alber alphonse sadek2; abd elrahman mohamed moussa1 | ||||
| 1Mechanical Engineering Department, Helwan University, Helwan, Egypt. | ||||
| 2Edison Welding Institute, EWI, Ohio, USA. | ||||
| Abstract | ||||
| Automatic inspection of welded gas pipelines is desirable because human inspectors are not always consistent evaluators. In addition, automatic inspection decreases the cost of inspection process and improves the inspection quality. In this paper, a proposed system named Phased Array for Characterizing Discontinuities “PACD” which combined 2D S-scan images into 3D images (volumetric scan) is more reliable and accurate and easily interpretation. The system is checking the shape, location, width and length of each discontinuity and the echo width and height of the A-scan. After that PACD inputs them to learned artificial neural network (ANN) to characterize 12-types of common welding discontinuities in gas pipelines welded by shield metal arc welding (SMAW). After verification the PACD system can be easily characterized welding discontinuities types with zero error. | ||||
| Keywords | ||||
| Welding discontinuities; Phased Array; ANN | ||||
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