Browsing by Author "Barsoum, Zuheir"

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Automated defect detection in digital radiography of aerospace welds using deep learning
(2022-04) Tyystjärvi, Topias; Virkkunen, Iikka; Fridolf, Peter; Rosell, Anders; Barsoum, Zuheir
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Aerospace welds are non-destructively evaluated (NDE) during manufacturing to identify defective parts that may pose structural risks, often using digital radiography. The analysis of these digital radiographs is time consuming and costly. Attempts to automate the analysis using conventional computer vision methods or shallow machine learning have not, thus far, provided performance equivalent to human inspectors due to the high reliability requirements and low contrast to noise ratio of the defects. Modern approaches based on deep learning have made considerable progress towards reliable automated analysis. However, limited data sets render current machine learning solutions insufficient for industrial use. Moreover, industrial acceptance would require performance demonstration using standard metrics in non-destructive evaluation, such as probability of detection (POD), which are not commonly used in previous studies. In this study, data augmentation with virtual flaws was used to overcome data scarcity, and compared with conventional data augmentation. A semantic segmentation network was trained to find defects from computed radiography data of aerospace welds. Standard evaluation metrics in non-destructive testing were adopted for the comparison. Finally, the network was deployed as an inspector’s aid in a realistic environment to predict flaws from production radiographs. The network achieved high detection reliability and defect sizing performance, and an acceptable false call rate. Virtual flaw augmentation was found to significantly improve performance, especially for limited data set sizes, and for underrepresented flaw types even at large data sets. The deployed prototype was found to be easy to use indicating readiness for industry adoption.
A guideline for fatigue strength improvement of high strength steel welded structures using high frequency mechanical impact treatment
(2013) Marquis, Gary B.; Barsoum, Zuheir
A4 Artikkeli konferenssijulkaisussa
In the past decade, high frequency mechanical impact (HFMI) has significantly developed as a reliable, effective and user-friendly method for post-weld fatigue strength improvement technique for welded structures. This paper presents a proposed fatigue design and assessment guideline for HFMI improved welded joints. Stress analysis methods based on nominal stress, structural hot spot stress and effective notch stress are discussed. The document especially considers the observed extra benefit that has been experimentally observed for HFMI treated high strength steels. The proposal is considered to apply to steel structures from plate thickness 5 to 50 mm and for yield strengths ranging from 235 MPa to 960 MPa. Several fatigue assessment examples are also provided. Lessons learned concerning appropriate HFMI procedures and quality assurance measures are presented. Due to differences in the HFMI tools and the wide variety of potential applications, certain details of a proper treatment procedures and quantitative quality control measures are presented generally. It is proposed that specific details should be documented in a HFMI Procedure Specification for each structure being treated. It is hoped that this guideline proposal will provide a stimulus to researchers working in the field. (C) 2013 The Authors. Published by Elsevier Ltd.