SBIR/STTR Award attributes
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.
