Thermal-cycling stability of LPBF 304L brazing fixtures: Microstructure, mechanical properties, and pin-hole dimension evolution
Source abstract
Abstract
Laser powder bed fusion (LPBF) offers an efficient route for manufacturing 304L stainless steel brazing fixtures, but repeated service in vacuum brazing furnaces raises concerns regarding their mechanical properties and structural stability. In this work, the microstructural, mechanical, and dimensional responses of LPBF-fabricated 304L brazing fixtures to repeated thermal cycling between 980°C and room temperature were studied by comparing an as-built condition and a substrate-constrained pre-annealed condition. The as-built state exhibited the highest initial strength, with a yield strength of about 505.09 MPa and an ultimate tensile strength of 797.17 MPa, owing to its dense dislocation-cell substructure, high crystal defects density. However, this state was thermally unstable. Subsequent cycling caused the disappearance of the dislocation-cell structure, grain coarsening, strength reduction, and pronounced pin-hole shrinkage from 7.138 ± 0.023 mm to 6.994 ± 0.014 mm after 15 cycles. By contrast, pre-annealing removed the cellular substructure in advance and shifted the material into a more recovered and stable state. As a result, later thermal cycling produced only limited additional microstructural change and much smaller dimensional drift, with the pin-hole diameter changing from 7.188 ± 0.008 mm to 7.108 ± 0.004 mm after 15 cycles. These results show that the dimensional instability of LPBF brazing fixtures mainly originates from the relaxation of the metastable as-built crystal defects. For fixture-type components, pre-annealing is therefore more beneficial than retaining the highest as-built strength.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Advanced Welding Techniques Analysis · Cellular and Composite Structures
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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