Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model

Shi, Yonghua; Wang, Zi-Shun; Chen, Xi-Yin; Cui, Yanxin; Xu, Tao; Wang, Jinyi

doi:10.1007/s40436-023-00437-1

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…It measures the cosine of the angle between two vectors, which can be useful for understanding the similarity between two series regardless of their magnitude. Another simple and commonly used metric is the Pearson correlation [ 28 ]. It can be used to assess the similarity of vectors regardless of their average values and variability, but at the same time, this type of correlation assumes that the relationship is linear and that the data are normally distributed, which might not always be the case in real-world data.…”

Section: Resultsmentioning

confidence: 99%

“…This high workload cannot be omitted by sample generation using, e.g., finite-elementmethod simulation. This is the case because in simulations for fixed sets of parameters, the same results will always be obtained [26][27][28]. During welding for the same set of parameters, slightly different results are gathered due to differences in the material structure or temporary changes in process conditions [29][30][31].…”

Section: Introductionmentioning

confidence: 96%

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Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates

Górka,

Jamrozik,

Wyględacz

et al. 2024

Sensors

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Maintaining high-quality welded connections is crucial in many industries. One of the challenges is assessing the mechanical properties of a joint during its production phase. Currently, in industrial practice, this occurs through NDT (non-destructive testing) conducted after the production process. This article proposes the use of a virtual sensor, which, based on temperature distributions observed on the joint surface during the welding process, allows for the determination of hardness distribution across the cross-section of a joint. Welding trials were conducted with temperature recording, hardness measurements were taken, and then, neural networks with different hyperparameters were tested and evaluated. As a basis for developing a virtual sensor, LSTM networks were utilized, which can be applied to time series prediction, as in the analyzed case of hardness value sequences across the cross-section of a welded joint. Through the analysis of the obtained results, it was determined that the developed virtual sensor can be applied to predict global temperature changes in the weld area, in terms of both its value and geometry changes, with the mean average error being less than 20 HV (mean for model ~35 HV). However, in its current form, predicting local hardness disturbances resulting from process instabilities and defects is not feasible.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates

Górka,

Jamrozik,

Wyględacz

et al. 2024

Sensors

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show abstract

Penetration identification of magnetic controlled Keyhole Tungsten inert gas horizontal welding based on OCR-SVM

Li,

Shi,

Wang

2024

Weld World

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Real-Time Recognition of Molten Pools Based on Improved DeepLabV3+ in Keyhole Tungsten Inert Gas Welding Applications

Yang,

Dai,

Cui

et al. 2024

Electronics

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During the Keyhole Tungsten Inert Gas (K-TIG) welding process, a significant amount of information related to the weld quality can be obtained from the weld pool and the keyhole of the topside molten pool image, which provides a vital basis for the control of welding quality. However, the topside molten pool image has the unstable characteristic of strong arc light, which leads to difficulty in contour extraction. The existing image segmentation algorithms cannot satisfy the requirements for accuracy, timing, and robustness. Aiming at these problems, a real-time recognition method, based on improved DeepLabV3+, for identifying the molten pool more accurately and effectively was proposed in this paper. First, MobileNetV2 was selected as the feature extraction network with which to improve detection efficiency. Then, the atrous rates of atrous convolution layers were optimized to reduce the receptive field and balance the sensitivity of the model to molten pools of different scales. Finally, the convolutional block attention module (CBAM) was introduced to improve the segmentation accuracy of the model. The experimental results verified that the proposed model had a fast segmentation speed and higher segmentation accuracy, with an average intersection ratio of 89.89% and an inference speed of 103 frames per second. Furthermore, the trained model was deployed in a real-time system and achieved a real-time performance of up to 28 frames per second, thus meeting the real-time and accuracy requirements of the K-TIG molten pool monitoring system.

show abstract

Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model

Cited by 3 publications

References 47 publications

Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates

Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates

Penetration identification of magnetic controlled Keyhole Tungsten inert gas horizontal welding based on OCR-SVM

Real-Time Recognition of Molten Pools Based on Improved DeepLabV3+ in Keyhole Tungsten Inert Gas Welding Applications

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