Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation

Abstract

The process-structure–property relationship for additive manufacturing (AM) is typically derived starting from the temperature profile which can be achieved by the meso-scale thermal-fluid flow simulation with huge computational cost. We propose a data-driven prognostic approach with specialized physical constraints to rapidly predict the temperature profiles. The dataset is constructed from the physics-based thermal-fluid flow simulation results under different manufacturing parameters. The temperature field around the molten pool region is statistically characterized by the function parameters of the individual isotherms, which are essentially the output of the data-driven model based on the input manufacturing parameters, while the temperature field is reconstructed using the interpolation approach based on the predicted isotherms. The data-driven predicted temperature profiles are validated against those from the thermal-fluid flow simulations, and then further applied in the thermal stress and grain growth simulations, of which the results are compared with those using the temperature profile directly from thermal-fluid flow simulations. The results demonstrate that our data-driven approach is highly feasible in predicting the geometry features of the isotherms and temperature profiles around the molten pool regions.