Determination of Optimal Process Parameters for L-PBF Produced 1.2709 Alloy

For L-PBF (Laser Powder Bed Fusion) metal additive manufacturing (AM) the choice of available materials is still limited. 1.2709 maraging steel powders are widely used for injection molds and high-quality engineering parts. The quality of the parts produced by L-PBF are significantly affected by the process parameters. The aim of this research was to find optimal process parameters for producing 1.2709 tool steel at a layer thickness of 20 µm and to reveal the possible parameter settings yielding comparable build quality as the “EOS surface” parameter set at a layer thickness of 20 µm. Findings showed that too low or too high input energies produce improper parts. A large range of parameters produce good quality parts, of which the optimum parameters can be chosen.

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Published
2025-10-29
DOI
https://doi.org/10.3390/engproc2025113017
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
0.00

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article

Determination of Optimal Process Parameters for L-PBF Produced 1.2709 Alloy

András Lajos Nagy, István Hatos, Imre Fekete, Balázs Lőrincz
Additive Manufacturing Materials and Processes
article

Determination of Optimal Process Parameters for L-PBF Produced 1.2709 Alloy

András Lajos Nagy, István Hatos, Imre Fekete, Balázs Lőrincz
article en

Abstract

For L-PBF (Laser Powder Bed Fusion) metal additive manufacturing (AM) the choice of available materials is still limited. 1.2709 maraging steel powders are widely used for injection molds and high-quality engineering parts. The quality of the parts produced by L-PBF are significantly affected by the process parameters. The aim of this research was to find optimal process parameters for producing 1.2709 tool steel at a layer thickness of 20 µm and to reveal the possible parameter settings yielding comparable build quality as the “EOS surface” parameter set at a layer thickness of 20 µm. Findings showed that too low or too high input energies produce improper parts. A large range of parameters produce good quality parts, of which the optimum parameters can be chosen.

Széchenyi István University (HU)
Szegedi Tudományegyetem
Openalex Percentile: Top 67%
Additive Manufacturing Materials and Processes
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