Demonstrates improved hardness in Alloy 718 using in-situ precipitation hardening, suggesting better manufacturing efficiency.
Metal additive manufacturing (AM) enables rapid, on-demand production of parts ranging from prototypes to mission-critical components. However, achieving high-strength metallic components often relies on post-processing heat treatments for many alloys, adding time and cost. For instance, in Alloy 718, a common high-strength alloy used in laser powder bed fusion (L-PBF) AM, strengthening primarily relies on precipitation hardening during an aging heat treatment. Importantly, heating during the deposition process can also elevate temperatures into the precipitation hardening range, causing in-situ aging. Thus, this work leverages the elevated temperatures during fabrication to enable controlled in-situ aging that increases as-fabricated hardness and improves uniformity. For the first time, this is realized through full-part thermal history control during L-PBF fabrication of Alloy 718. The method embeds an experimentally fitted material hardening model in an axisymmetric lumped-layer thermal simulation to predict in-situ part hardness. The resulting thermal and hardness dynamics model is then used in conjunction with a trajectory optimization algorithm to determine time-varying laser power and baseplate temperature profiles. These optimized process conditions target a uniform hardness of 450 HV in an inverted cone geometry by intentionally inducing in-situ precipitation hardening. The planned trajectory increased the mean hardness and improved uniformity from 374 ± 41 HV to 439 ± 29 HV without a separate post-process aging heat treatment. These results are repeatable within 8% and establish a path to integrate microstructural aging control directly into the deposition step to achieve high-strength metallic components without an additional post-process heat treatment step. • Demonstrated high-throughput time–temperature-hardness characterization methodology. • Applied a modified Avrami equation to model continuous cooling transformations. • Predicted part hardness during printing using thermal history simulations. • Determined optimal power and baseplate temperature to maximize hardness.
No takes yet. Share an insight, caveat, or question.
Templeton et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: