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

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-23-C-0114
Agency Tracking Number: N21B-T022-0096
Amount: $799,999.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N21B-T022
Solicitation Number: 21.B
Timeline
Solicitation Year: 2021
Award Year: 2023
Award Start Date (Proposal Award Date): 2022-11-08
Award End Date (Contract End Date): 2024-12-09
Small Business Information
1 Airport Place, Suite 1
Princeton, NJ 08540-1111
United States
DUNS: 610056405
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jinhui Yan
 (858) 281-9801
 yjh@illinois.edu
Business Contact
 Jim Lua
Phone: (860) 398-5620
Email: jlua@gem-innovation.com
Research Institution
 The Board of Trustees of the University of Illinois Urbana-Champaign Campus
 Susan Martinis
 
1901 South First Street, Suite A
Champaign, IL 61820-7406
United States

 (217) 333-2187
 Nonprofit College or University
Abstract

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.

* Information listed above is at the time of submission. *

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