Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 2037-2042· 0 citations· 21 references
Abstract
Welding helps in the joining of two materials using high temperature and filler material. The material's complete properties depend on the welding quality, which makes the product more resilient towards the strong factors. It is common to encounter faults in welded materials; therefore, to address this issue, a visual inspection of the product is carried out. The development of Artificial Intelligence enables the automated detection of weld defects. Deep learning models are mainly used for defect detection, especially of CNNs. This paper proposes an explanatory model for the classification process of the Vision Transformer using ResNet18 feature extraction on the welding surface images dataset. The proposed work, using patches and attention, achieves efficient classification, and Local Interpretable Model Explanation is used to explain the features responsible for the defect and to rectify it. By this method, the proposed work showed 92.96% accuracy and 92.82% F1 score.
An empirical comparison between three deep learning frameworks for pixel-based surface defect detection: a baseline CNN architecture known as U-Net, Vision Transformer (ViT) based on patch-wise attention mechanism, and TransUNet that combines CNN and transformer architecture.
The traditional approach to detecting weld defects in industrial films requires sophisticated equipment and extensive manual labor. Not only is this process time-consuming, but it also poses risks to human vision, and the results are highly dependent on the operator’s level of technical expertise. To address this issue...
Wen-Fei Chen, Binbin Li, Binhua He et al.· Journal of Physics, Conferen...· 0 citations
A multi modal temporal attention based deep learning defect detection model for internal defects that are challenging to detect, including porosity, lack of penetration and fusion, undercut, and cold lap during Gas Metal Arc Welding in fillet joints is proposed.
M. Mobaraki, M. Asadi, K. van Heusden et al.· 0 citations
The rapid advancement of intelligent textile manufacturing has sped up the development of automatic fabric fault detection systems. The present textile industry is increasingly moving toward intelligent textile production, and the development of autonomous fabric flaw detection systems is speeding up. This can assist l...
J. Raja, S.Krishnan· 2026 International Conferenc...· 0 citations
. Welding quality is crucial to components' safety and lifespan, while welding defects are unavoidable due to process parameters, material properties, and manual operations. Visual inspection via computer vision and deep learning well compensates for traditional techniques' shortcomings. This paper reviews visual-based...
Cheng-Ge Shi· Proceedings of the 3rd Inter...· 0 citations
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