Data-driven benchmarking methodology for evaluating PBF-LB/M machines with RMSD Analysis
: Nadeem, Usama; Kamboj, Nikhil; Nayak, Chinmayee; Piili, Heidi
: Nadimpalli, Venkata Karthik; Mohanty, Sankhya; Jensen, Dorte Juul; Defer, Marion Caroline; Pan, Zhihao
: Nordic Laser Materials Processing Conference
Publisher: IOP Publishing
: 2025
IOP Conference Series: Materials Science and Engineering
: 20th Nordic Laser Materials Processing Conference
: 012040
: 1332
: 1757-8981
: 1757-899X
DOI: https://doi.org/10.1088/1757-899X/1332/1/012040
: https://doi.org/10.1088/1757-899x/1332/1/012040
: https://research.utu.fi/converis/portal/detail/Publication/505437587
Laser based powder bed fusion for metals (PBF-LB/M) is an industrial additive manufacturing (AM) method offering high -precision manufacturing for complex geometries. However, comparing the performance of different PBF-LB/M machines remains difficult, especially when machines are from different manufacturers. This study introduces a new benchmark artifact with standard features for facilitating the evaluation and comparison of machine performance. Two industrial PBF-LB/M machines, EOS M290 and Aconity3D MIDI+, were used to fabricate the part under similar conditions. The additively manufactured (AMed) samples were then inspected using 3D scanning metrology tools, and the results were analyzed using a method called root mean square deviation (RMSD) to measure how far each feature deviates from the original design. The results showed apparent differences in how each machine handled certain features and provide useful information for choosing the right machine based on part geometry.
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This work was conducted under the “Kestävästi lisäävä!” project (Turku Innovation Centre of Additive Manufacturing, TICAM), funded by the European Regional Development Fund via the Helsinki-Uusimaa Regional Council (decision A80276). The project runs from May 1, 2023, to June 30, 2026, in collaboration with University of Turku, AÅ bo Akademi University, Turku University of Applied Sciences, and industry partners. The authors gratefully acknowledge the project and its partners for their support in advancing SME additive manufacturing capabilities in Turku region.
