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  1. Abstract

    Precipitation strengthening of alloys by the formation of secondary particles (precipitates) in the matrix is one of the techniques used for increasing the mechanical strength of metals. Understanding the precipitation kinetics such as nucleation, growth, and coarsening of these precipitates is critical for evaluating their hardening effects and improving the yield strength of the alloy during heat treatment. To optimize the heat treatment strategy and accelerate alloy design, predicting precipitate hardening effects via numerical methods is a promising complement to trial-and-error-based experiments and the physics-based phase-field method stands out with the significant potential to accurately predict the precipitate morphology and kinetics. In this study, we present a phase-field model that captures the nucleation, growth, and coarsening kinetics of precipitates during isothermal heat treatment conditions. Thermodynamic data, diffusion coefficients, and misfit strain data from experimental or lower length-scale calculations are used as input parameters for the phase-field model. Classical nucleation theory is implemented to capture the nucleation kinetics. As a case study, we apply the model to investigate γ″ precipitation kinetics in Inconel 625. The simulated mean particle length, aspect ratio, and volume fraction evolution are in agreement with experimental data for simulations at 600 °C and 650 °C duringmore »isothermal heat treatment. Utilizing the meso-scale results from the phase-field simulations as input parameters to a macro-scale coherency strengthening model, the evolution of the yield strength during heat treatment was predicted. In a broader context, we believe the current study can provide practical guidance for applying the phase-field approach as a link in the multiscale modeling of material properties.

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  2. The presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6Al-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multiphysics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power, and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have the largest impact on the microstructure variation. Through this exemplar process optimization, the current study also demonstrates the promising potential of the presented approach in facilitating other complicate AM process optimizations, such as robust designs in terms of porosity control or direct mechanical property control.