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Multi-physics modeling of the 2022 NIST additive manufacturing benchmark (AM-Bench) test series

  • Qiming Zhu,
  • Ze Zhao,
  • Jinhui Yan

摘要

This paper presents an effective high-fidelity multi-physics model for metal additive manufacturing (AM). Using a mixed interface-capturing/interface-tracking approach, the model integrates level set and variational multiscale formulation for thermal multi-phase flows and explicitly handles the gas-metal interface evolution without mesh motion and re-meshing schemes. We integrate the mixed formulation with an energy-conservative ray tracing-based laser model and a mass-fixing algorithm that accounts for phase transitions. First, we present the mathematical details of the proposed model. Then, we apply the model to simulate the NIST A-AMB2022-01 Benchmark test, emphasizing the prediction of thermal history, laser absorption rate, melt pool dimensions, and pore formation. The results show the model’s strong capability to accurately capture the complex physics of metal AM processes and its potential in simulation-based process optimization.