Quantifying Uncertainty in the Mechanical Performance of Additively Manufactured Parts Due to Material and Process Variation

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-16-C-0249
Agency Tracking Number: N16A-004-0055
Amount: $84,966.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N16A-T004
Solicitation Number: 2016.0
Solicitation Year: 2016
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-06-16
Award End Date (Contract End Date): 2017-01-31
Small Business Information
1820 Ridge Avenue, Evanston, IL, 60201
DUNS: 088176961
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Jeff Doak
 (847) 425-8248
Business Contact
 Voula Colburn
Phone: (847) 425-8215
Email: vcolburn@questek.com
Research Institution
 Lehigh University
 Raymond Genellie, Jr.
 526 Brodhend Avenue
Office of Research and Sponsored Programs
Bethlehem, PA, 18015-3046
 (847) 425-8211
 Nonprofit college or university
Additive manufacturing (AM) promises to be an innovative technology that can enable rapid manufacturing of complicated parts and greatly reduced cycle time. However, the AM process is complex and involves a large number of processing steps, each with its own set of uncertainties. These uncertainties compound through the AM build process, resulting in parts with widely varying properties across different machines, over time for the same machine, and locally within one part. Improving the build quality of these AM parts and achieving MMPDS B-basis certification requires quantifying and managing the uncertainty in AM parts and materials.
QuesTek Innovations, LLCs Accelerated Insertion of Materials (AIM) methodology rapidly qualifies materials for specific applications by coupling modeling/simulation/testing across all stages of component and material development. In this Navy STTR, QuesTek will extend AIM to laser powder bed AM of Ti-6Al-4V by: 1) Adding a Bayesian framework within AIM to propagate input and model uncertainties through Integrated Computational Materials Engineering (ICME) models. 2) Refining mechanistic processstructureproperty models for AM Ti-6-4. Propagating uncertainty through these models will give yield strength probability distributions and confidence intervals. The ultimate outcome of this tool will be a more rapid insertion of AM components into Navy technologies.

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

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