Process parameter optimization for enhanced mechanical performance in PBF-LB/M of AISI 316L using constant energy density

Laser powder bed fusion (PBF-LB/M) is an additive manufacturing technology (AM) for producing metal components with complex geometries. The mechanical properties of these components play an important role in their performance and functionality. This work has found the process parameters to achieve the optimal properties of AISI 316L stainless steel using SamyLabs Alba 300 system. A Design of Experiments approach was employed under a constant volumetric energy density constraint to identify optimal parameter combinations (laser power, laser frequency, scanning speed and hatching distance). Tensile testing and metallographic analysis show that even at fixed energy input, the material’s properties were still affected by the process parameters. Specifically, higher laser frequencies and reduced scanning speeds, combined with increased hatching distances, led to improved mechanical performance with a significant positive impact on tensile strength and hardness of the manufactured parts. The results demonstrate that laser frequency plays a critical role in melt pool dynamics. Optimal settings derived from this study provide a reliable guideline for enhancing part quality in PBF-LB/M processes and can be extrapolated to similar AM systems.

Authors

Institutions

Publication Details

Journal
Progress in Additive Manufacturing
Published
2026-05-29
DOI
https://doi.org/10.1007/s40964-026-01659-y
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Process parameter optimization for enhanced mechanical performance in PBF-LB/M of AISI 316L using constant energy density

Alejandro López, Beatriz Achiaga, Lorenzo Pedrolli, Daniel Gomez-Lendinez et al.
Progress in Additive Manufacturing
Additive Manufacturing Materials and Processes
article

Process parameter optimization for enhanced mechanical performance in PBF-LB/M of AISI 316L using constant energy density

Alejandro López, Beatriz Achiaga, Lorenzo Pedrolli, Daniel Gomez-Lendinez, Rafael Barea, Blanca Castaño
article en

Abstract

Laser powder bed fusion (PBF-LB/M) is an additive manufacturing technology (AM) for producing metal components with complex geometries. The mechanical properties of these components play an important role in their performance and functionality. This work has found the process parameters to achieve the optimal properties of AISI 316L stainless steel using SamyLabs Alba 300 system. A Design of Experiments approach was employed under a constant volumetric energy density constraint to identify optimal parameter combinations (laser power, laser frequency, scanning speed and hatching distance). Tensile testing and metallographic analysis show that even at fixed energy input, the material’s properties were still affected by the process parameters. Specifically, higher laser frequencies and reduced scanning speeds, combined with increased hatching distances, led to improved mechanical performance with a significant positive impact on tensile strength and hardness of the manufactured parts. The results demonstrate that laser frequency plays a critical role in melt pool dynamics. Optimal settings derived from this study provide a reliable guideline for enhancing part quality in PBF-LB/M processes and can be extrapolated to similar AM systems.

Progress in Additive Manufacturing
Universidad de Deusto (ES), Universidad Nebrija (ES)
Universidad de Deusto
Affordable and clean energy
Openalex Percentile: Top 10%
Additive Manufacturing Materials and Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.