Laser powder bed fusion dataset for relative density prediction of commercial metallic alloys
Year: 2025
Type: data-paper
Abstract: Laser-based powder bed fusion (L-PBF) technology stands out for its ability to create complex, high-performance parts, optimizing design freedom and material efficiency. Despite technical and financial challenges, it is attractive to industries where performance, weight reduction, and customization are critical. In L-PBF, relative density (RD) is a key factor that directly impacts the mechanical properties and overall quality of printed parts. However, predicting RD is a complex and costly task due to the numerous factors involved. This study addresses this need by creating a large-scale dataset for RD prediction in L-PBF, consisting of 1579 samples of commercial alloys from the literature. It includes printing conditions and other crucial inputs like protective atmosphere, powder size distribution, and part geometry. This dataset offers a valuable resource for researchers to benchmark their results, better understand key factors influencing RD, and validate models or explore new machine-learning approaches tailored to L-PBF.
Source: Scientific Data
Authors: Germán Omar Barrionuevo, Iván La Fé-Perdomo, Jorge A. Ramos-Grez
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W4408157297