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Innovative Multi-scale/Multi-physics Model for Surface Finish Prediction and Optimization of Metal Additively Manufacture Parts

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-21-C-0168
Agency Tracking Number: N19B-034-0042
Amount: $1,099,983.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N19B-T034
Solicitation Number: 19.B
Solicitation Year: 2019
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-06-15
Award End Date (Contract End Date): 2024-06-28
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
 Lei Yan
 (703) 226-4068
Business Contact
 Scott Bradfield
Phone: (703) 226-4061
Research Institution
 University of Louisville
 Brennan Cox
300 E. Market Street, Suite 300
Louisville, KY 40202-1959
United States

 (502) 852-8806
 Nonprofit College or University

In this STTR effort, TDA and its team partner University of Louisville will focus on developing an innovative intelligent decision support tool using data-driven multi-scale multi-physics (D2M2) models to derive process-surface roughness-fatigue relationships for selective laser melting (SLM). The proposed models account for both powder characteristics and AM processing/path planning, including powder size distribution, laser power, scanning speed, scanning strategy, geometry features, and build orientation. Critical experiments will be performed during the course of this research as part of verification and validation. The goal of this D2M2 computational framework is to predict and optimize component-level surface roughness within a reasonable time. Proposed D2M2 computational framework will address surface roughness caused by rippling marks, balling effect, staircase effect, and sintered powders. Solution for the D2M2 framework will be obtained by discrete element method (DEM), computational fluid dynamics (CFD), finite element method (FEM) and data-driven modeling.

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

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