Laser Welding of dissimilar cell tabs: Extracting physics semantics from infrared (IR) emissions as process monitoring data

Sabatakakis, K.; Bourlesas, N.; Bikas, H.; Papacharalampopoulos, A.; Stavropoulos, P.

doi:10.1016/j.procir.2023.09.251

Procedia CIRP

2024

DOI: 10.1016/j.procir.2023.09.251

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Laser Welding of dissimilar cell tabs: Extracting physics semantics from infrared (IR) emissions as process monitoring data

K. Sabatakakis,

N. Bourlesas,

H. Bikas

et al.

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2024

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Cited by 3 publications

References 26 publications

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Criticalities in the Laser Welding of Li-ion Batteries

Srinivas,

Babu,

Kumar

et al. 2024

Trans Indian Inst Met

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Criticalities in the Laser Welding of Li-ion Batteries

Srinivas,

Babu,

Kumar

et al. 2024

Trans Indian Inst Met

View full text Add to dashboard Cite

Multi-model feature aggregation for classification of laser welding images with vision transformer

Din,

Zhang,

Nawaz

et al. 2024

Journal of King Saud University - Computer and Information Scie

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A Multi-Layer Multi-Pass Weld Bead Cross-Section Morphology Extraction Method Based on Row–Column Grayscale Segmentation

Lei,

Gong,

2024

Materials

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In the field of welding detection, weld bead cross-section morphology serves as a crucial indicator for analyzing welding quality. However, the extraction of weld bead cross-section morphology often relies on manual extraction based on human expertise, which can be limited in consistency and operational efficiency. To address this issue, this paper proposes a multi-layer multi-pass weld bead cross-section morphology extraction method based on row–column grayscale segmentation. The weld bead cross-section morphology image is pre-processed and then segmented into rows and columns based on the average gray value of the image. In order to extract the feature of multi-layer multi-pass weld feature images, an outline showing the binarization threshold is selected for each segmented image (ESI). Then, the weld contour of ESI is extracted before image fusion and morphological processing. Finally, the weld feature parameters (circumference, area, etc.) are extracted from the obtained weld feature image. The results indicate that the relative errors in circumference and area are within 10%, while the discrepancies in maximum weld seam width and maximum weld seam height can be close to the true value. The quality assessment falls within a reasonable range, the average value of SSIM is above 0.9 and the average value of PSNR is above 60 on average. The results demonstrate that this method is feasible for extracting the general contour features of multi-layer multi-pass weld bead cross-section morphology images, providing a basis for further detailed analysis and improvement in welding quality assessment.

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Laser Welding of dissimilar cell tabs: Extracting physics semantics from infrared (IR) emissions as process monitoring data

Cited by 3 publications

References 26 publications

Criticalities in the Laser Welding of Li-ion Batteries

Criticalities in the Laser Welding of Li-ion Batteries

Multi-model feature aggregation for classification of laser welding images with vision transformer

A Multi-Layer Multi-Pass Weld Bead Cross-Section Morphology Extraction Method Based on Row–Column Grayscale Segmentation

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