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Abstract Nanograined metals have the merit of high strength, but usually suffer from low work hardening capacity and poor thermal stability, causing premature failure and limiting their practical utilities. Here we report a “nanodispersion-in-nanograins” strategy to simultaneously strengthen and stabilize nanocrystalline metals such as copper and nickel. Our strategy relies on a uniform dispersion of extremely fine sized carbon nanoparticles (2.6 ± 1.2 nm) inside nanograins. The intragranular dispersion of nanoparticles not only elevates the strength of already-strong nanograins by 35%, but also activates multiple hardening mechanisms via dislocation-nanoparticle interactions, leading to improved work hardening and large tensile ductility. In addition, these finely dispersed nanoparticles result in substantially enhanced thermal stability and electrical conductivity in metal nanocomposites. Our results demonstrate the concurrent improvement of several mutually exclusive properties in metals including strength-ductility, strength-thermal stability, and strength-electrical conductivity, and thus represent a promising route to engineering high-performance nanostructured materials. 

Abstract 316L stainless steel (316L SS) is a flagship material for structural applications in corrosive environments, having been extensively studied for decades for its favorable balance between mechanical and corrosion properties. More recently, 316L SS has also proven to have excellent printability when parts are produced with additive manufacturing techniques, notably laser powder bed fusion (LPBF). Because of the harsh thermo-mechanical cycles experienced during rapid solidification and cooling, LPBF processing tends to generate unique microstructures. Strong heterogeneities can be found inside grains, including trapped elements, nano-inclusions, and a high density of dislocations that form the so-called cellular structure. Interestingly, LPBF 316L SS not only exhibits better mechanical properties than its conventionally processed counterpart, but it also usually offers much higher resistance to pitting in chloride solutions. Unfortunately, the complexity of the LPBF microstructures, in addition to process-induced defects, such as porosity and surface roughness, have slowed progress toward linking specific microstructural features to corrosion susceptibility and complicated the development of calibrated simulations of pitting phenomena. The first part of this article is dedicated to an in-depth review of the microstructures found in LPBF 316L SS and their potential effects on the corrosion properties, with an emphasis on pitting resistance. The second part offers a perspective of some relevant modeling techniques available to simulate the corrosion of LPBF 316L SS, including current challenges that should be overcome. 

Laser-powder bed fusion additive manufacturing (LPBF-AM) of metals is rapidly becoming one of the most important materials processing pathways for next-generation metallic parts and components in a number of important applications. However, the large parametric space that characterizes laser-based LPBF-AM makes it challenging to understand what are the variables controlling the microstructural and mechanical property outcomes. Sensitivity studies based on direct LPBF-AM processing are costly and lengthy to conduct, and are subjected to the specifications and variability of each printer. Here we develop a fast-throughput numerical approach that simulates the LPBF-AM process using a cellular automaton model of dynamic solidification and grain growth. This is accompanied by a polycrystal plasticity model that captures grain boundary strengthening due to complex grain geometry and furnishes the stress-strain curves of the resulting microstructures. Our approach connects the processing stage with the mechanical testing stage, thus capturing the effect of processing variables such as the laser power, laser spot size, scan speed, and hatch width on the yield strength and tangent moduli of the processed materials. When applied to pure Cu and stainless 316L steel, we find that laser power and scan speed have the strongest influence on grain size in each material, respectively.