Achieving of ultra high power laser deep welding for 20 mm thick Inconel 690 alloy: Process optimization, non-uniformity of microstructure and mechanical properties
Source abstract
Abstract
Welding thick plates is challenging via traditional arc welding due to the high accumulated welding heat input and thermal deformation. Ultra-high power laser welding is a promising method for thick-walled structures because of the high welding penetration depth, precision, and efficiency. In this work, a systematic study was conducted on welding process parameters of Inconel 690 alloy with 20mm thickness using 30 kW fiber laser welding machine. The influence of welding parameters on the weld formation of thick plate was studied, it was found that the laser power and speed determine the weight of liquid metal, while the defocusing amount affect the migration distance of liquid metal in the molten pool. The welding defects such as welding root sagging and excessive penetration were effectively resolved through process optimization, the welded joint with good formation was obtained and inspected by radiography, showing excellent weld quality without porosities and cracks under nuclear eligibility criteria. The correlation between microstructure and properties was systematically investigated via molten pool dynamics. Along the weld thickness direction, the weld microstructure is significantly inhomogeneous, grain sizes are larger near the weld front side and back side, and smaller in the middle area. More fine TiN precipitates were observed between dendrites in the weld middle, but fewer on the front and back side. This is consistent with the trend of hardness distribution. The hardness in the FZ middle region reaches 205 HV, which is approximately 20.6% higher than that in the surface regions.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Welding Techniques and Residual Stresses · Additive Manufacturing Materials and Processes · Advanced Welding Techniques Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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