We propose an automatic welding defect detection system, which is a computer software application, using Radiation Sourced images (gamma rays). Our software can take the image of a pipe with its welding defects and compare them to an optimal database where the “perfect” constitution of the image is stored, and indicate deficiencies in real-time, using Machine Learning Algorithms (ML). Our proposed method for automatic detection and classification of faults and defects in the radiographic images of welded joints is attained through an exposure technique of double-wall image (DWDI).
Our product is a software application to be offered on an annual subscription basis. The proposed method consists of preliminary radiographical images, subsequently fed through our software to detect anomalies and flaws in real-time. It is a computer-aided software for automatic fault detection using Radiation Sourced images. This proposed software contributes towards the improvement of automatic detection and assists weld inspectors in the preparation of technical reports.
A solution to do a non-destructive test on the welding area of pipelines to interpret and show any defect happened. Using computer technology just in the site, it saves costs for the companies by deleting the normal procedure steps and saving time. Using the source of Big Data, it will present a specific outcome, which will empower the users to have an idea of the dangerous and risky defect before they face with it.
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