A real spatial–temporal attention denoising network for nugget quality detection in resistance spot weld

Zhou, Jie; Xi, Zhen; Wang, Shilong; Yang, Bo; Zhang, Youhong; Zhang, Yucheng

doi:10.1007/s10845-023-02160-x

Cited by 3 publications

(2 citation statements)

References 50 publications

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“…The spatial attention weight is calculated according to the regressed boundary distance fields, and the calculated weight is fused into the feature map to enhance the significance of the welding zone. Zhou et al [98] constructed a deep learning model named real spatial-temporal attention denoising network (RSTADN), which consists of a denoising module, spatial-temporal attention modules, and multiple residual modules. The experimental results indicate that the accuracy of RSTADN in the task of detecting the quality of welded nuggets reaches as high as 94.35%.…”

Section: Attention Mechanism In Welding Sensingmentioning

confidence: 99%

Review on the Application of the Attention Mechanism in Sensing Information Processing for Dynamic Welding Processes

Xu,

Liu,

et al. 2024

JMMP

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Arc welding is the common method used in traditional welding, which constitutes the majority of total welding production. The traditional manual and manual teaching welding method has problems with high labor costs and limited efficiency when faced with mass production. With the advancement in technology, intelligent welding technology is expected to become a solution to this problem in the future. To achieve the intelligent welding process, modern sensing technology can be employed to effectively simulate the welder’s sensory perception and cognitive abilities. Recent studies have advanced the application of sensing technologies, leading to the advancement in intelligent welding process. The review is divided into two aspects. First, the theory and applications of various sensing technologies (visual, sound, arc, spectral signal, etc.) are summarized. Then, combined with the generalization of neural networks and attention mechanisms, the development trends in welding sensing information processing and modeling technology are discussed. Based on the existing research results, the feasibility, advantages, and development direction of attention mechanisms in the welding field are analyzed. In the end, a brief conclusion and remarks are presented.

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Section: Attention Mechanism In Welding Sensingmentioning

confidence: 99%

Review on the Application of the Attention Mechanism in Sensing Information Processing for Dynamic Welding Processes

Xu,

Liu,

et al. 2024

JMMP

View full text Add to dashboard Cite

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“…Even though the image signals are greatly useful, they can only be applied to identify surface defect problems; critical inner defects cannot be detected. Meanwhile, the lower interpretability for deep learning [10,20,30] and imbalanced data for RSW processes [7] are gradually drawing more and more attention from researchers. However, research on these processes are still scarce.…”

Section: Related Researchmentioning

confidence: 99%

A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies

Chang,

Zhou,

Ding

et al. 2023

Mathematics

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Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies.

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Road surface crack detection based on improved YOLOv5s

Ding,

Jiao,

et al. 2024

MBE

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<abstract> <p>In response to the issues of low efficiency and high cost in traditional manual methods for road surface crack detection, an improved YOLOv5s (you only look once version 5 small) algorithm was proposed. Based on this improvement, a road surface crack object recognition model was established using YOLOv5s. First, based on the Res2Net (a new multi-scale backbone architecture) network, an improved multi-scale Res2-C3 (a new multi-scale backbone architecture of C3) module was suggested to enhance feature extraction performance. Second, the feature fusion network and backbone of YOLOv5 were merged with the GAM (global attention mechanism) attention mechanism, reducing information dispersion and enhancing the interaction of global dimensions features. We incorporated dynamic snake convolution into the feature fusion network section to enhance the model's ability to handle irregular shapes and deformation problems. Experimental results showed that the final revision of the model dramatically increased both the detection speed and the accuracy of road surface identification. The mean average precision (mAP) reached 93.9%, with an average precision improvement of 12.6% compared to the YOLOv5s model. The frames per second (FPS) value was 49.97. The difficulties of low accuracy and slow speed in road surface fracture identification were effectively addressed by the modified model, demonstrating that the enhanced model achieved relatively high accuracy while maintaining inference speed.</p> </abstract>

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A real spatial–temporal attention denoising network for nugget quality detection in resistance spot weld

Cited by 3 publications

References 50 publications

Review on the Application of the Attention Mechanism in Sensing Information Processing for Dynamic Welding Processes

Review on the Application of the Attention Mechanism in Sensing Information Processing for Dynamic Welding Processes

A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies

Road surface crack detection based on improved YOLOv5s

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