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자료유형
학술저널
저자정보
김태원 (계명대학교) 최해운 (계명대학교)
저널정보
대한용접·접합학회 대한용접·접합학회지 大韓熔接·接合學會誌 第39卷 第1號
발행연도
2021.2
수록면
67 - 73 (7page)

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This study reports on Al-6061 and oxygen-free copper C1020P joining results and analysis using cross-sectional metallography and a weld bead deep learning algorithm. The state-of-the-art green laser in the visible region (λ = 515 nm) was used as a welding heat source, and the reliability of the bonding interface was analyzed. Remarkable spatter reduction was achieved in the full scan-speed range of 180 - 220 mm/s and output of 800–1200 W. Using a green laser with 40% absorption, we achieved high-quality joining without employing any additional mechanical processes such as weaving, wobble, or oscillation. In the case of an IR laser, we determined that it was sensitive to the state of the surface (e.g., scratched or rough). By contrast, in the case of the green laser, it was relatively insensitive, and a homogeneous bead was formed. Over 98% accuracy was found for the welded parts, and 66% accuracy was observed for the failed welding parts. The welding quality was derived as a deterministic rather than stochastic result, and it was confirmed that image-based deep learning technology was effectively applied and could be used for non-destructive welding quality inspection.

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Abstract
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2. 실험방법
3. 실험 결과
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