Weld Defect Detection and Classification based on Deep Learning Method: A Review

Weld Defect Detection and Classification based on Deep Learning Method: A Review

weld defect, radiographic images, deep learning, convolutional neural network

The inspection of weld defects utilizing nondestructive testing techniques based on radiography is essential for ensuring the operability and safety of weld joints in metals or other materials. During the process of welding, weld defects such as cracks, cavity or porosity, lack of penetration, slag inclusion,and metallic inclusion may occur. Due to the limitations of manual interpretation and evaluation, recent research has focused on the automation of weld defect detection and classification from radiographic images. The application of deep learning algorithms enables automated inspection. The deep learning architectures for building weld defect classification models were discussed. This paper concludes with a discussion of the achievements of automation methods and a presentation of the research recommendations for the future.

Tito Wahyu Purnomo, Finkan Danitasari, Djati Handoko

http://dx.doi.org/10.21609/jiki.v16i1.1147

Faculty of Computer Science Universitas Indonesia

2023-02-28

Sri Wahyuni

e-ISSN : 2502-9274 printed ISSN : 2088-7051

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Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)