Seamless Numerical Simulation for Laser Powder Bed Fusion Process by Lattice Boltzmann and S. Nomoto 1, J. Katagiri 1, M. Kusano 1, T. Kitashima 1 and M. Watanabe1 Research Center for Structural Materials, National Institute for Materials Science (NIMS) A three dimensionally integrated numerical method using lattice Boltzmann method and multi-phase field method is proposed for simulating melting and solidification of metal alloy in laser powder bed additive manufacturing process. Modified lattice kinetic scheme that is a kind of lattice Boltzmann method is applied to simulate gas and fluid flows with free surface. Melting and solidification of metal alloy are modelled by multi-phase field method with consideration of grain anisotropy. Conserved Allen-Cahn equation, which is also transformed to the modified lattice kinetic scheme formulation, is adapted to track the liquid-solid free surface moving dynamics. Thermal equation with heat source of traveling laser beam is solved by coupling fluid flow, melting and solidification analyses. Highly parallelized computational program is developed by MPI and OpenMP hybrid methods. A three-dimensional single-track model for Ni alloy is built with consisting of atmosphere gas, multi-grain base plate, and powder regions. Ni alloy powder is modeled by Discreet Element Method. Simulations are performed in conditions of different beam power values. Simulated solidified microstructures are confirmed to be qualitatively agreement with experimental measurements. A Neural Network Accelerated Kinetic Chemical Order in CrCoNi M Jun-Ping Du 1 and Shigenobu Ogata 1 1 Department of Mechanical Science and Bioengineering, Osaka University, Osaka 560-8531, Japan The neural network potential (NNP) has been used to study the local structures of chemical order in the high-entropy alloys (HEAs) and the medium-entropy alloys (MEAs). However, little research has been conducted to reveal the kinetics of chemical ordering via vacancy diffusion by using NNP in kinetic Monte Carlo (KMC) simulations due to the heavy computation expense in estimating the vacancy diffusion energy barrier. Here, we developed an estimator of the vacancy diffusion energy barrier using the neural network method to accelerate the energy barrier computation in the KMC simulations. Using the neural network accelerated KMC simulations, the evolution of the chemical order in the CrCoNi MEA was simulated at various annealing temperatures. It was found that the short-range order was formed above 800 K, while below the temperature a chemical domain structure was displayed. The local structures of the chemical order obtained in the KMC simulations, such as the Cr/CoNi {100}, Cr/CoNi {110}, and Cr/CoNi {113} types of the superlattice, agree with those found by the density functional theory calculations and the experiments. With these simulations as a basis, time−temperature−chemical-order diagrams were drawn, which provide key information for controlling the chemical order through thermal processing. 1 Multi-Phase Field edium-entropy Alloy Poster Presentation ｜NIMS Award Symposium 2023Methods Monte Carlo Simulation of the Evolution of P4 ｜ ModelingPP44--1133 PP44--1144 75
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