Quantifying the effects of gap on the molten pool and porosity formation in laser butt welding

Guo, Liping; Wang, Hongze; Liu, Hanjie; Huang, Yuze; Wei, Qianglong; Leung, Chu Lun Alex; Wu, Yi; Wang, Haowei

doi:10.1016/j.ijheatmasstransfer.2023.124143

Cited by 6 publications

(2 citation statements)

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“…In terms of weld seam quality and process stability, the gap size is of fundamental importance [3]. By affecting the energy absorption within the keyhole and the conditions of melt and vapor flow [4,5], the joint gap has a major impact on the pressure balance of the keyhole, which is crucial for keyhole stability [6]. As a result, the formation of weld imperfections (e.g., spatter, pores) can differ depending on the gap size [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…By affecting the energy absorption within the keyhole and the conditions of melt and vapor flow [4,5], the joint gap has a major impact on the pressure balance of the keyhole, which is crucial for keyhole stability [6]. As a result, the formation of weld imperfections (e.g., spatter, pores) can differ depending on the gap size [4,5]. It can therefore be concluded that the detection This study addresses a significant gap in the field of laser beam butt welding, which is the absence of open-source datasets and the underexplored use of airborne acoustic analysis spanning from audible to ultrasonic ranges.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring of Joint Gap Formation in Laser Beam Butt Welding using Neural Network-Based Acoustic Emission Analysis

Gourishetti,

Schmidt,

Römer

et al. 2023

Crystals

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This study aimed to explore the feasibility of using airborne acoustic emission in laser beam butt welding for the development of an automated classification system based on neural networks. The focus was on monitoring the formation of joint gaps during the welding process. To simulate various sizes of butt joint gaps, controlled welding experiments were conducted, and the emitted acoustic signals were captured using audible-to-ultrasonic microphones. To implement an automated monitoring system, a method based on short-time Fourier transformation was developed to extract audio features, and a convolutional neural network architecture with data augmentation was utilized. The results demonstrated that this non-destructive and non-invasive approach was highly effective in detecting joint gap formations, achieving an accuracy of 98%. Furthermore, the system exhibited promising potential for the low-latency monitoring of the welding process. The classification accuracy for various gap sizes reached up to 90%, providing valuable insights for characterizing and categorizing joint gaps accurately. Additionally, increasing the quantity of training data with quality annotations could potentially improve the classifier model’s performance further. This suggests that there is room for future enhancements in the study.

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