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2025 (English)In: Materials & design, ISSN 0264-1275, E-ISSN 1873-4197, Vol. 259, article id 114805Article in journal (Refereed) Published
Abstract [en]
Beam shaping is considered a technology capable of dramatically improving quality and robustness of Laser Metal Fusion (LMF) processes. However, systematic investigations of its effects on melt-pool dynamics, temperature field and microstructure are still required. In this work, we propose an integrated approach combining a Computational Fluid Dynamics (CFD) model, in-situ temperature measurements and metallographic analysis to explore programmable ring beam profiles, ranging from Gaussian-dominant to ring-dominant configurations. This method, initially proposed on Ti-6Al-4V bead-on-plate tracks, validates melt-pool temperatures measured in-process by a dual-wavelength pyrometer against CFD predictions, which are in turn validated with metallographic cross-sections. Ring modes lowered peak temperature by up to 35 %, transforming deep-narrow pools (aspect ratio approximate to 0.9) into shallow-wide ones (approximate to 0.4). This suppressed humping at line energies >= 0.28 J mm-1, whereas lower energies produced only superficial melting. Simulations matched pyrometer data within 5 % whenever pool width equalled the pyrometers’ sensing spot; all tracks solidified into ultrafine alpha with retained beta, independent of beam mode. Therefore, the combination of in-situ, ex-situ and CFD tools offers a practical workflow for assisting data-driven process optimization and can be easily extended to other LMF processes, with its potential implementation in industrial Laser Powder Bed Fusion.
Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Dual-wavelength pyrometry; CFD simulations; Ti-6Al-4V; Laser Metal Fusion (LMF); Beam shaping; Electron Backscatter Diffraction (EBSD)
National Category
Manufacturing, Surface and Joining Technology
Research subject
Production Technology
Identifiers
urn:nbn:se:hv:diva-24456 (URN)10.1016/j.matdes.2025.114805 (DOI)001582316500003 ()2-s2.0-105020860737 (Scopus ID)
Funder
Swedish Research Council, 2022-06725
Note
CC-BY 4.0
The computations and related data handling were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725.
2026-01-092026-01-092026-01-21