Integrated Computational Materials Engineering (ICME) Modeling Tool for Optimum Gas Flow in Metal Additive Manufacturing Processes

Global Engineering and Materials, Inc. and Professor Jinhui Yan at the University of Illinois at Urbana-Champaign propose to develop an Integrated Computational Materials Engineering simulation toolkit for optimal tailoring of gas flow in laser powder bed fusion (L-PBF) and direct energy deposition (DED) to reduce defects (e.g., porosity and spatter) and surface roughness improve quality (e.g., microhardness and heat-affected zone). The proposed tool, Additive Manufacturing Gas Flow Simulator (AM-GFS), quantifies the gas flow characteristics such as nozzle flow in DED and gas circulation in PBF, and predicts defect/quality index for the component-level print as typical in the aircraft. The model capability highlights are summarized as follows: 1) multi-scale model that couples the gas flow phenomena in powder-scale and chamber-/nozzle-scale. 2) High-fidelity powder-scale physics modeling that resolves the laser absorption, molten pool, vapor jet, gas entrainment, and spattered particles. 3) Full validation using in-situ and ex-situ data (e.g., surface profile, spatter count, and molten pool size). 4) Physics-informed machine learning (ML) based surrogate models that are trained based on simulation data to fast produce process-to-defect relationship. 5) Cross-process models which are robust to accommodate both DED and PBF processes. The results from the AM-GFS tool will establish a process map that delineates the boundaries of high defect index region in a gas-flow parameter space. Such capability will accelerate the process design iterations to identify the optimal gas flow that minimizes defects.