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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: N68335-21-C-0863
Agency Tracking Number: N21B-T022-0042
Amount: $139,922.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N21B-T022
Solicitation Number: 21.B
Solicitation Year: 2021
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-09-27
Award End Date (Contract End Date): 2022-04-04
Small Business Information
3190 Fairview Park Drive Suite 650
Falls Church, VA 22042-4549
United States
DUNS: 010983174
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Anahita Imanian
 (703) 226-4078
Business Contact
 Scott Bradfield
Phone: (703) 226-4061
Research Institution
 Carnegie Mellon University
 Jennifer Sopic
5000 Forbes Ave.
Pittsburgh, PA 15213-3815
United States

 (412) 268-8746
 Nonprofit College or University

Users of additive manufacturing machines expect the highest quality when it comes to the mechanical properties of parts, the usability of the machines and associated processes, and overall machine design. The main contributor to the quality of the part is the involvement of process-related by-products originating from the melting process. To handle these by-products in additive manufacturing - in the case of laser powder bed fusion (LPBF) - an efficient gas flow over the build plate is required to enable high build rates, clean melting processes, and effective evacuation of the by-products, such as soot and spatter. In this STTR effort, the TDA team proposes to develop a comprehensive toolset based on an Integrated Computational Material Engineering (ICME) framework that enables optimizing the gas flow, including improvement in nozzle designs; gas circulation to match the design of the AM machine offering optimum shielding of the fusion area and the melt pool; and the efficient removal of the gas and debris from the chamber. The toolset also provides ways to set print parameters for optimum part performance for the raw material used and the scan patterns for the part. The toolset uses advanced computational fluid dynamic models and machine learning (ML)-based algorithms to model gas flow, spatter particle, and melt pool interactions. The key products from the proposed framework are: fluid-particle interaction model to adjust gas flow parameters to efficiently remove spatter and soot from the chamber; gas-melt pool dynamic interaction models to model melt pool dynamic, spatter and defect generation; microstructure and mechanical response models to understand the influence of gas flow on the cooling rate, heat affected area, microstructure, and material properties; ML-based algorithms for real-time detection of spatter particles and their trajectories via processing advanced in-situ monitoring data; chamber nozzle design models to control the gas flow; and the optimization framework for optimizing gas flow, print parameters, and scan patterns.

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

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