Improving Laser Powder Bed Fusion IN718 Process Development Efficiency by Eliminating Pore Defects of Specified Size

The rapid identification of process windows in laser powder bed fusion (L-PBF) additive manufacturing garnered significant attention for its ability to reduce upfront engineering costs. This study focuses on accelerating the development of process windows by targeting the elimination of specific-size pore defects in L-PBF IN718. A novel relative density-porosity similarity evaluation method (DPSEM) is introduced to evaluate the reliability of porosity data derived from computed tomography (CT). Using the response surface method, the fully dense forming window (e.g., relative density ≥ 99%) was accurately located within a wide process parameter range (18-1000 J/mm<sup>3</sup>) in a single test. Comparative analysis with the relative density (RD) model highlighted differences in solution set distribution, positioning efficiency, microstructure, and performance within the process window. Results demonstrate that the proposed method effectively eliminates specified size defects (90 μm), achieving a maximum density of 99.5% alongside excellent mechanical properties, including an ultimate tensile strength of 1155 MPa and a yield strength of 908 MPa. In contrast, the RD model achieved a lower maximum density of 98.5%, with mechanical performance compromised by significant MC compound precipitation and keyhole pore accumulation, resulting in an ultimate tensile strength slightly exceeding 910 MPa.

Authors

Institutions

Publication Details

Journal
Materials
Published
2025-04-24
DOI
https://doi.org/10.3390/ma18091929
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

Improving Laser Powder Bed Fusion IN718 Process Development Efficiency by Eliminating Pore Defects of Specified Size

Rui Han, Song Zhao, Kaiyue Ma, Qianyu Ji et al.
Materials
Additive Manufacturing Materials and Processes
article

Improving Laser Powder Bed Fusion IN718 Process Development Efficiency by Eliminating Pore Defects of Specified Size

Rui Han, Song Zhao, Kaiyue Ma, Qianyu Ji, Yaru Zhang, Yihui Zhang, Yuzhong Wang, Wenxian Li, Bingheng Lu, Wenhua Guo, Chenwei Wang
article en

Abstract

The rapid identification of process windows in laser powder bed fusion (L-PBF) additive manufacturing garnered significant attention for its ability to reduce upfront engineering costs. This study focuses on accelerating the development of process windows by targeting the elimination of specific-size pore defects in L-PBF IN718. A novel relative density-porosity similarity evaluation method (DPSEM) is introduced to evaluate the reliability of porosity data derived from computed tomography (CT). Using the response surface method, the fully dense forming window (e.g., relative density ≥ 99%) was accurately located within a wide process parameter range (18-1000 J/mm<sup>3</sup>) in a single test. Comparative analysis with the relative density (RD) model highlighted differences in solution set distribution, positioning efficiency, microstructure, and performance within the process window. Results demonstrate that the proposed method effectively eliminates specified size defects (90 μm), achieving a maximum density of 99.5% alongside excellent mechanical properties, including an ultimate tensile strength of 1155 MPa and a yield strength of 908 MPa. In contrast, the RD model achieved a lower maximum density of 98.5%, with mechanical performance compromised by significant MC compound precipitation and keyhole pore accumulation, resulting in an ultimate tensile strength slightly exceeding 910 MPa.

MaterialsVol. 18(9)
Xi'an Jiaotong University (CN)
Shaanxi Provincial Science and Technology Department
Industry, innovation and infrastructure
Openalex Percentile: Top 92%
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.