Online Monitoring of Welding Status Based on a DBN Model During Laser Welding

Zhang, Yanxi; You, Deyong; Gao, Xiangdong; Katayama, Seiji

doi:10.1016/j.eng.2019.01.016

Cited by 51 publications

(13 citation statements)

References 27 publications

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“…With the development of science and technology, welding technology has been applied to various fields, such as the automobile industry, machinery manufacturing, the shipbuilding industry, aerospace, biomedicine, powder metallurgy, the microelectronics industry, energy generation, and other fields [1][2][3]. In the welding process, due to the influence of the weldment surface condition, joint gap [4], welding power [5], welding speed [6], and other factors, welded products are prone to welding defects, which directly affect the products' quality [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Spot welding is used to weld individual points on the contact surface of the weldment at the joint. In industrial production, spot welding is mainly used in the following aspects: (1) overlap in sheet metal stamping parts, (2) thin plate and section steel structures and skin structures, (3) screens, space frames, and cross steel bars, and so on [10,11]. In order to ensure the quality of products and projects, while avoiding accidents, non-destructive inspection of welds is required [12].…”

Section: Introductionmentioning

confidence: 99%

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Magneto-Optical Imaging of Arbitrarily Distributed Defects in Welds under Combined Magnetic Field

Gao

Tian

et al. 2022

Metals

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Using the traditional magneto-optical detection methods, micro-weld defects parallel with the magnetic field direction may be overlooked. In order to overcome this, a non-destructive testing method based on magneto-optical imaging under a vertical combined magnetic field (VCMF) is proposed. To demonstrate this, the experimental results of the magneto-optical imaging of weld defects excited by a vertical combined magnetic field (VCMF) or parallel combined magnetic field (PCMF) are compared with those of traditional magnetic fields (constant magnetic field (CMF), alternating magnetic field (AMF), and rotating magnetic field (RMF)). It is found that the magneto-optical imaging under a VCMF can accurately detect weld defects of any shape and distribution. In addition, the center difference method is used to eliminate the influence of noise on the defect contour extraction of magneto-optical images, and the active contour of weld defects in the magneto-optical images is extracted. The results show that many noises can be identifiedby the robustness of the level set method, operating in low-pass filtering, so that much information that is usually lost can be retained.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Magneto-Optical Imaging of Arbitrarily Distributed Defects in Welds under Combined Magnetic Field

Gao

Tian

et al. 2022

Metals

Self Cite

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“…In the existing researches, four kinds of sensors are mainly used to monitor the laser welding process, such as visual [14][15][16], acoustic [17][18][19], optical [20,21] and thermal [22,23] sensors. These sensors are used to capture the changes of characteristic quantities during the welding process [24][25][26][27] and establish a correlation between the changing trends of characteristic quantities and the final welding effect.…”

Section: Introductionmentioning

confidence: 99%

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

Fan

Peng

Zhou

et al. 2021

Sensors

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Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data set of laser welding process is often difficult to build and there is not enough experimental data, which hinder the applications of the data-driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten-class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN-CNN and ACGAN-SVM-CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real-time classification and recognition through data enhancement and multi-model fusion.

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“…In this study, laser cleaning was adopted to remove the oxide layer on the hot-rolled stainless steel. LIBS, which allows the fast elemental characterization on the surface of the material without sample preparation [20][21][22], was used to monitor the cleaning process online. The phase composition and morphology of the as-received and laser cleaned surfaces were characterized to illustrate the surface cleaning quality.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Monitoring of Laser Cleaning for Hot-Rolled Stainless Steel by Laser-Induced Breakdown Spectroscopy

Xing

2021

Metals

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Laser cleaning is a competitive alternative to ablate and remove the hard oxide layer on hot-rolled stainless steel. To meet the practical demand, laser-induced breakdown spectroscopy (LIBS) was applied for real-time monitoring of the cleaning process in this study. Furthermore, the as-received and laser cleaned surfaces were characterized by an optical micrograph, an X-ray diffractometer, and a laser scanning confocal microscope. The results showed the relative intensity ratio (RIR) of the FeI emission line at 520.9 nm and the CrI emission line at 589.2 could be a quantitative index to monitor the cleaning process. When the oxide layer was not fully cleaned, the LIBS signals of the substrate were not excited, and the ratio was almost invariant as the power of the laser increased. However, it sharply increased once the oxide layer was effectively cleaned, the cleaned surface was bright, and the surface roughness was smaller in this case. Subsequently, as the surface was over-cleaned with the further increase of laser power, the RIR value remained large. The optimal laser cleaning parameters obtained by the monitoring were determined to avoid re-oxidation and reduce the roughness of the cleaned surface.

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Online Monitoring of Welding Status Based on a DBN Model During Laser Welding

Cited by 51 publications

References 27 publications

Magneto-Optical Imaging of Arbitrarily Distributed Defects in Welds under Combined Magnetic Field

Magneto-Optical Imaging of Arbitrarily Distributed Defects in Welds under Combined Magnetic Field

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

Real-Time Monitoring of Laser Cleaning for Hot-Rolled Stainless Steel by Laser-Induced Breakdown Spectroscopy

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