Influence mechanism of the keyhole behavior on penetration depth by in-situ monitoring in pulsed laser welding of aluminum alloy

Wen, Xianhai; Wu, Di; Zhang, Peilei; Liu, Sikai; Luo, Zhongyi; Jia, Zhiyuan; Ye, Xin; Shi, Haichuan; Yu, Zhishui

doi:10.1016/j.ijleo.2021.167812

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…To verify the rationality of the acoustic source mechanism, the keyhole depth was extracted under different laser powers with a common imaging-processing algorithm according to our previous studies [ 32 , 33 ]. From a series of variation curves of keyhole depths in each laser pulse (see Figure 4 b), we found that: (1) when the laser energy is lower (3 kW), the depth variation of keyhole increases from 0.7 mm to 1.5 mm.…”

Section: Methodical Approachmentioning

confidence: 99%

Laser Welding Penetration Monitoring Based on Time-Frequency Characterization of Acoustic Emission and CNN-LSTM Hybrid Network

Luo

Zhang

et al. 2023

Materials

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In-process penetration monitoring of the pulsed laser welding process remains a great challenge for achieving uniform and reproducible products due to the highly complex nature of the keyhole dynamics within the intense laser-metal interactions. The main purpose of this study is to investigate the feasibility of acoustic emission (AE) measurement for penetration monitoring based on acoustic wave characteristics and deep learning. Firstly, a series of laser welding experiments on aluminum alloys were conducted using high-speed photography and AE techniques. This allowed us to in-situ visualize the complete keyhole dynamics and elucidate the generation mechanism of acoustic waves originating from pressure fluctuations at the keyhole wall. Then, an adaptive time-frequency technique namely VMD (Variational Mode Decomposition) was proposed to characterize the acoustic energy distribution among the nine subsignals with low-frequency and high-frequency components under different welding penetrations. Lastly, a novel hybrid model combing CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory) was designed to deeply mine the spatial and temporal acoustic features from the extracted frequency components. Extensive experiments demonstrate that our proposed approach yields a remarkable classification performance with a test accuracy of 99.8% and a standard deviation of 0.21, which obtains a high recognition rate. This work is a new paradigm in the digitization and intelligence of the laser welding process and contributes to an alternative way of developing an efficient end-to-end penetration monitoring system.

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Section: Methodical Approachmentioning

confidence: 99%

Laser Welding Penetration Monitoring Based on Time-Frequency Characterization of Acoustic Emission and CNN-LSTM Hybrid Network

Luo

Zhang

et al. 2023

Materials

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“…In contrast to the co-axial camera system, off-axial cameras with a more flexible angle can be developed to gather information about the keyhole and melt pool, as well as plume and spatters during LBW process. For instance, our research team have performed a series of long-term studies on the process monitoring of laser-based manufacturing, such as remote laser welding [74], pulsed laser spot/seam welding [75]- [76], [124], [202], laser-arc hybrid welding [77]- [79] and laser melting deposition [80]. By establishing an off-axial visual sensing platform consisting of the high-speed camera and laser illumination system, we can clearly capture the images of keyhole/melt pool and vapor plumes, and further investigate the metal melting/ solidification phenomena and the underlying mechanisms of the occurrence of welding defects (cracks and porosity), which as shown in Fig.…”

Section: Off-axial Visual Sensingmentioning

confidence: 99%

Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling

Zhang

et al. 2022

Journal of Manufacturing Processes

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“…At present, it is necessary to find the appropriate welding parameters through a large number of tests, which not only wastes a lot of manpower and time, but also hardly meet the requirements of various plate combination design. Therefore, it is very important to find out the influencing factors and rules of weld forming coefficient [1,5].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of welding laser beam and its influence on weld forming coefficient

Han,

Jia,

2024

J. Phys.: Conf. Ser.

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Laser welding is a very complicated process. At present, there is no systematic research on the relationship between welding parameters and forming coefficient. By studying the internal relationship between laser parameters and the characteristics of welding laser, laser beam indexes, such as focusing index, are defined. They are simplified to a few laser parameters so as to facilitate the study of the influence of welding parameters on the forming coefficient. The results show that material, assembly gap, laser power and welding speed have great influence on weld penetration. When the laser power density reaches 106J/cm2, the characteristics of deep penetration welding appear. Under the condition of constant clearance, the weld depth increases with the increase of t/b. The weld penetration increases with the increase of laser power and tends to be stable when the welding speed is 12mm/s. Different materials also have a certain influence on the weld penetration depth. Under the same conditions, X6CrNiTi1810 obtains greater penetration depth, indicating that physical and chemical properties of materials are also one of the factors affecting the penetration depth. The mechanism and condition of laser welding were analysed. The relationship between laser absorptivity of sheet surface and physical properties of the materials is also studied and then obtained. The study will provide theoretical guidance for laser welding process design and welding parameter selection of steel plate.

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Influence mechanism of the keyhole behavior on penetration depth by in-situ monitoring in pulsed laser welding of aluminum alloy

Cited by 15 publications

References 30 publications

Laser Welding Penetration Monitoring Based on Time-Frequency Characterization of Acoustic Emission and CNN-LSTM Hybrid Network

Laser Welding Penetration Monitoring Based on Time-Frequency Characterization of Acoustic Emission and CNN-LSTM Hybrid Network

Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling

Characteristics of welding laser beam and its influence on weld forming coefficient

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