Using Interpolation with Nonlocal Autoregressive Modeling for Defect Detection in Welded Objects

Amir, Mohammad; Mirzapour, Mahdi; Yahaghi, Effat

doi:10.1007/s10921-020-00704-2

Cited by 3 publications

(1 citation statement)

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“…Another critical aspect affecting the effectiveness of defect classification is based on the selection of the classifier [14]. Since the majority of the features of the weld defects are extracted manually, several researchers focused on automatically detecting welding defects using a learning approach [15]. Convolutional neural networks (CNN) applications have begun to rise, as shown in figure 3.…”

Section: Defect Classificationmentioning

confidence: 99%

Weld defect identification and characterization in radiographic images using deep learning: Review

Bansal¹,

Vettivel

Kumar³

et al. 2023

Eng. Res. Express

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Defects in the welds degrade the quality of the weld. Weld defect identification is a challenging task in the industry because of the wide range of weld imperfections. Weld defect detection using radiographic images is an effective technique for achieving good weld quality in shipbuilding and aerospace applications. Foreign inclusions, cracks and pores are examples of welding joint imperfections. Several appropriate computer-based image processing techniques have made the detection of weld defects possible. It is challenging because weld imperfection can show various sizes, shapes, contrasts and locations in radiography images. The accuracy of this inspection process is more dependent on various external factors and is also time-consuming. Automatic weld defect detection is needed by analyzing the images obtained directly from digital radiographic systems. This paper uses a unique image-based approach to a small batch of X-ray imaging datasets to investigate a potential solution for weld defect identification. This article compares a deep learning network's performance for various parameter and hyper-parameter combinations. Also it compares the traditional approaches of defect detection using manual inspection method, feature-based defect identification, and finally deep - learning based approach on several types of weld defects in various industrial applications. This comparative analysis concludes that deep learning-based approaches have achieved more accuracy as compared to conventional techniques. This research paper also highlights a few challenges and future directions in welding area.

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Section: Defect Classificationmentioning

confidence: 99%