Insights into primary carbides and nanoparticles in an additively manufactured high-alloy steel
Source abstract
Abstract
This paper reports the use of combined electron microscopy and small-angle neutron scattering (SANS) to probe into the compositional and size evolution of the primary carbides and nanoparticles (defined by a size threshold of 100 nm) in an additively manufactured high-alloy steel before and after heat treatment. The primary carbides exhibit marginal changes in their total volume fraction and size after the austenitising and tempering. However, those carbides located at the prior-austenite boundaries provide a pinning effect to impede grain growth during the austenitising. The grain size increases from 1.7 µm in the as-manufactured to 2.8 µm in the as-tempered conditions, resulting in a limited strength loss of 33-126 MPa as estimated by using the Hall-Petch relationship. Atom probe tomography, which offers atomic spatial resolution examining a sample volume of 6.6 × 105 nm3, reveals the presence of numerous V and Cr-enriched nanoparticles with sizes of 1 to 10 nm within the steel matrix after the tempering. In contrast, the complementary SANS which examines a significantly larger sample volume of 0.9 mm3 but lacking spatial resolution, provides nanoparticle size information. It reveals a radius reduction from 7.6 to 1.0 nm and a volume fraction increase from 1.6% to 2.3%, in the as-manufactured and as-tempered conditions, respectively. The nanoparticles induced during tempering can contribute to a strength enhancement of 691 MPa, primarily through the Orowan bypass mechanism. This suggests that the combination of limited prior-austenite grain growth and the presence of nanoparticles is the key factor responsible for the unprecedented material strength.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Advanced materials and composites · Advanced Materials Characterization Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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