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| Home > Publications database > Analysis of laser beam welding with superimposed 445 and 1070 nm wavelength lasers on copper by in situ synchrotron diagnostics |
| Home > Publications database > Analysis of laser beam welding with superimposed 445 and 1070 nm wavelength lasers on copper by in situ synchrotron diagnostics |
| Journal Article | PUBDB-2025-00593 |
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2024
Laser Inst. of America
Orlando, Fla.
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Please use a persistent id in citations: doi:10.2351/7.0001598 doi:10.3204/PUBDB-2025-00593
Abstract: In laser welding, precision and reproducibility are fundamentally dependent on temporal and spatial processes of energy input. Induced by the dynamics of the melt pool, pressure equilibria in the vapor capillary, and solidification behavior, different weld seam qualities are achieved. To obtain the lowest possible defect frequency, new tailored joining strategies need to be investigated using multibeam and multiwavelength approaches. To improve the quality by influencing the process dynamics, a dual-beam approach is investigated that superimposes a stationary laser beam with a wavelength of 445 nm with a spatially modulated laser beam with a wavelength of 1070 nm. The aim is to utilize ∼10 times higher absorption of a 445 nm diode laser on copper with the high focusability of a 1070 nm fiber laser. In this context, the influence of the relative positions of the two beams to each other on the weld seam quality is investigated, while one of the beams moves either in front, behind, or coaxial to the other beam following the path of a line weld. The main objective is to observe how the laser beams influence each other and how the capillary depth and porosity vary for different parameters. To visualize the process dynamics, the welding experiments on copper are performed at the Deutsches Elektronen-Synchrotron DESY by means of in situ phase contrast videography. Quality-determining weld properties like the distribution of pores or process fluctuations are then extracted automatically from the image sequences by means of a trained neuronal network.
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