Abstract
This study investigated the microstructural and mechanical behavior of ultrahigh-strength 18Ni (300) maraging steel manufactured using laser powder bed fusion (L-PBF) and subjected to various heat treatments (HTs). Four distinct HTs, including direct aging (DA), solution treatment with aging (S-A), cyclic austenitization with aging (CA-A), and solution treatment prior to both CA and A (S-CA-A), were designed based on thermodynamic calculations and dilatation tests to enhance strengthening mechanisms. Microstructural characteristics were analyzed through electron backscatter diffraction, transmission electron microscopy, electron probe microanalysis, and X-ray diffraction. Mechanical properties such as hardness, tensile strength, and impact toughness were evaluated and correlated with the microstructure. The DA treatment significantly refined the microstructure and promoted austenite reversion, while the CA-A treatment resulted in a coarser structure with higher austenite content. The DA and CA-A treatments provided an optimal combination of strength (2 GPa) and moderate ductility (6%). Physical models were utilized to relate yield strengths to microstructural characteristics, highlighting the strengthening effect of second-phase particles. Empirical equations based on power law and exponential functions were assessed for modeling strain-hardening behavior. CA increased austenite fractions, with CA-A and S-CA-A cycles showing 43.7% and 46.3%, respectively. Laves-phase particles reduced the steel's impact energy absorption, decreasing toughness.
Original language | English |
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Article number | 114114 |
Journal | Materials and Design |
Volume | 254 |
DOIs | |
Publication status | Published - Jun 2025 |
MoE publication type | A1 Journal article-refereed |
Funding
The authors sincerely thank BUSINESS Finland for supporting this research study under the “DREAMS” project, No. 4795/31/2021.
Keywords
- Additive manufacturing
- Austenite reversion
- Heat treatment
- Maraging steel
- Microstructure optimization
- Physically-based modeling
- Strengthening precipitates