Surface condition driven fatigue performance of laser powder bed fusion H13 steel

Surface variation is an inherent imperfection in the laser powder bed fusion (LPBF) processed samples, comprising of primary roughness (PRR) from the melt pool solidification and secondary roughness (SER) from adhered powder particles. Components under cyclic loading are sensitive to surface defects, making surface finishing crucial for fatigue-bearing parts. However, this undermines LPBF’s advantage of creating high-performance and geometrically complex components. This study examines the effect of surface roughness on fatigue crack initiation in LPBF H13 steel, a high-strength alloy with a tensile strength of 1800 MPa. The focus is on scenarios where contour scanning strategies are not employed. Vertically deposited samples were subjected to three surface conditions: as-built (AB) with PRR+SER, half-polished (HP) with only PRR, and well-machined (M) with no PRR or SER. Optical and digital surface roughness characterization and stress-controlled fatigue testing were performed. Results revealed that while AB-samples showed significantly rough surface compared to HP-samples, their fatigue performance did not reduce. In contrast, M-samples had a slight improvement in roughness but exhibited a fatigue life increase by a factor greater than 17. This was attributed to the PRR, specifically the solidification valleys between the neighboring melt pools. Factographic analysis showed that the PRR-driven fatigue crack initiation is a critical determinant of fatigue performance.

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Publication Details

Journal
Journal of Materials Research and Technology
Published
2024-11-01
DOI
https://doi.org/10.1016/j.jmrt.2024.11.048
Citations
5
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
1.06

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article

Surface condition driven fatigue performance of laser powder bed fusion H13 steel

Mohamed El Mansori, Zehao Qin, Nan Kang
5 citations
Journal of Materials Research and Technology
Additive Manufacturing Materials and Processes
1.06
article

Surface condition driven fatigue performance of laser powder bed fusion H13 steel

Mohamed El Mansori, Zehao Qin, Nan Kang
article en
5 citations

Abstract

Surface variation is an inherent imperfection in the laser powder bed fusion (LPBF) processed samples, comprising of primary roughness (PRR) from the melt pool solidification and secondary roughness (SER) from adhered powder particles. Components under cyclic loading are sensitive to surface defects, making surface finishing crucial for fatigue-bearing parts. However, this undermines LPBF’s advantage of creating high-performance and geometrically complex components. This study examines the effect of surface roughness on fatigue crack initiation in LPBF H13 steel, a high-strength alloy with a tensile strength of 1800 MPa. The focus is on scenarios where contour scanning strategies are not employed. Vertically deposited samples were subjected to three surface conditions: as-built (AB) with PRR+SER, half-polished (HP) with only PRR, and well-machined (M) with no PRR or SER. Optical and digital surface roughness characterization and stress-controlled fatigue testing were performed. Results revealed that while AB-samples showed significantly rough surface compared to HP-samples, their fatigue performance did not reduce. In contrast, M-samples had a slight improvement in roughness but exhibited a fatigue life increase by a factor greater than 17. This was attributed to the PRR, specifically the solidification valleys between the neighboring melt pools. Factographic analysis showed that the PRR-driven fatigue crack initiation is a critical determinant of fatigue performance.

Journal of Materials Research and TechnologyVol. 33
Arts et Métiers (FR), HESAM Université (FR), Texas A&M Engineering Experiment Station
China Scholarship Council
Affordable and clean energy
Openalex Percentile: Top 26%
Additive Manufacturing Materials and Processes
1.06
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Surface condition driven fatigue performance of laser powder bed fusion H13 steel — Mohamed El Mansori, Zehao Qin, et al. · Journal of Materials Research and Technology (2024) | TGRS Research Map | TGRS