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Rapid, Low Cost, High-quality Component Qualification Using Multi-scale, Multi-physics Analytical Toolset for the Optimization of Metal Additive Manuf

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-17-C-0021
Agency Tracking Number: N162-083-0087
Amount: $149,991.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N162-083
Solicitation Number: 2016.2
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2016-10-20
Award End Date (Contract End Date): 2018-02-06
Small Business Information
1794 Olympic Parkway
Park City, UT 84098
United States
DUNS: 035206915
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Kai Zeng
 Principal Investigator
 (502) 836-7009
Business Contact
 Jon Ginn
Phone: (435) 799-4497
Research Institution

Additive Manufacturing (AM) is of increasing interest for production of complex geometries and high-performance components for the DoD due to its advantage of design freedom and as-needed production capability. Components of interest to the Navy have a high standard in porosity, surface finish, mechanical properties and dimensional tolerance, etc. However, mechanical properties and part quality variability issues inherent to AM make rapid qualification of metal AM parts difficult. AM processes rely heavily on trial and error experiments for part qualification which is time and cost consuming, and without guarantee of optimized process parameters. 3DSIM is in the process of commercializing AM predictive modeling products capable of predicting thermal histories, residual stress, support structure design and part qualities such as porosity, surface finish, dimensional tolerance and distortion, etc. for metal AM processes. To address Phase I objectives, an analytical software toolset will be developed with integration of new AM part quality optimization algorithms which take into account one or several process parameters and intersection of others. 3DSIM proposes to add the capability of optimizing AM process parameters to enable rapid, low cost, high quality component qualification as the missing link for using predictive modeling in a streamlined qualification environment.

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

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