因科镍合金
材料科学
冶金
极限抗拉强度
焊接性
复合材料
拉伸试验
粉末冶金
焊接
热的
作者
Daozheng Li,Luis Fernando Ladinos Pizano,Liyi Wang,Wei Xiong
出处
期刊:
[Elsevier BV]
日期:2026-01-01
卷期号:4: 100140-100140
标识
DOI:10.1016/j.smmf.2026.100140
摘要
Mixing commercially available alloy powders in Additive Manufacturing (AM) provides a versatile route for composition exploration, yet tailoring post-heat treatments for such mixtures remains challenging. In this study, we developed an efficient ICME-based high-throughput model framework to screen aging choice as a part of post-heat treatment design for a novel alloy blend between Stainless Steel 316L (SS316L) and Inconel 718 (IN718). The framework centers on a modified yield strength model that explicitly accounts for the non-spherical morphology of γ ′ ′ precipitates and their multi-modal size distributions during multi-step isothermal aging. We applied this approach to a 77 wt% IN718 and 23 wt% SS316L blend (77.IN718), selected for its potential for grain refinement during homogenization. After screening 1944 double-step aging profiles, the simulation identified an optimized treatment (725 °C/8 h + 600 °C/8 h) predicting a yield strength of 1070 MPa, which closely matches the experimental value. Despite a significant reduction in niobium and titanium content relative to standard Inconel 718, the new alloy 77.IN718 achieved performance (1055 MPa yield strength, 1285 MPa ultimate tensile strength, and 19.5% elongation) comparable to bulk IN718 processed by conventional manufacturing at room temperature. This work demonstrates a successful integration of ICME-enabled design and experimental validation, offering a sustainable and efficient pathway for developing high-performance alloys by blending existing commercial feedstocks. The methodology enables alloy development with reduced reliance on critical elements, enhancing supply chain resilience in resource-constrained environments.
科研通智能强力驱动
Strongly Powered by AbleSci AI