A laser hybrid welding process in which a defocused laser beam is applied beside a gas metal arc weld (GMAW) pool to modify the bead shape was studied. The present paper aims to produce welds with improved toe geometry and better fatigue life than those made with GMAW alone and to apply a numerical simulation to help configure the hybrid process. First, stationary hybrid welds were made to validate weld bead shape predictions and to characterise the spreading of the arc weld deposit to the laser heated spot. Next, the travelling hybrid process was configured with the aid of simulations and fatigue test specimens were welded. Proper application of the laser heat input induced molten metal to spread to the laser heated area, increasing the fillet weld leg length. This produced a larger weld toe angle that decreased the stress concentration and increased the fatigue life of the welds relative to standard mean values.
Laser technology has many advantages in welding for the manufacture of EV battery packs. Aluminum (Al) and copper (Cu) are welded using a dual laser beam, suggesting the optimum power distribution for the core and ring beams. Due to the very high re ectance of Cu and Al exposed to near-infrared lasers, the material absorbs a very small amount of energy. Compared to single beam laser welding, dual beam welding has signi cantly improved surface quality by controlling surface solidi cation. The study focused on the quality of weld surface beads, weld properties and tensile strength by varying the output ratio of the core beam to the ring beam. Optimal conditions of Al6061 were a 700 W core beam, a 500 W ring beam and 200 mm/s of weld speed. For the C1020P, the optimum conditions were a center beam of 2500 W, a ring beam of 3000 W and a welding speed of 200 mm/s. In laser lap welding of Al-Al and Al-Cu, the bead width and the interfacial bead width of the joint increased as the output increased. The penetration depth did not change signi cantly, but small pores were formed at the interface of the junction. Tensile tests were performed to demonstrate the reliability of the weld zone, and computer simulations provided analysis of the heat distribution for optimal heat input conditions.
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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