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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: N68335-18-C-0018
Agency Tracking Number: N16A-004-0227
Amount: $294,512.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N16A-T004
Solicitation Number: 16.A
Timeline
Solicitation Year: 2016
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-01-29
Award End Date (Contract End Date): 2021-04-15
Small Business Information
714 E Monument ave Suite 130
Dayton, OH 45402
United States
DUNS: 831845255
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Ayman Salem Dr. Ayman Salem
 President
 (937) 531-6658
 ayman.salem@icmrl.net
Business Contact
 Daniel Satko
Phone: (937) 705-0892
Email: dan.satko@icmrl.net
Research Institution
 Case Western Reserve University
 Ashley Solomon Ashley Solomon
 
10900 Euclid Ave
Cleveland, OH 44106
United States

 (216) 368-6480
 Nonprofit college or university
Abstract

Additive manufacturing is an extremely customizable process; however, variations in the chosen build parameters can lead to drastic differences in part performance. The performance variation due to process parameters is still not well understood, and propagating all uncertainties from the various sources has been a challenge. Sources of AM parts’ performance variability include uncertainties in feedstock, machine process parameters, material response during melting/solidification, process modeling, performance prediction, and post heat treatment.. Utilizing an ICME platform, MRL has developed a software to propagate uncertainty throughout various stages in the building cycle which was demonstrated and verified on quantifying uncertainty in distortion/residual stress and location-specific strength predictions in Ti6Al4V. The development of the software to include predictions of uncertainty in dynamic performance enhances its commercial use for DoD and non-DoD applications. . The flexibility of the software is developed in the ability to train the software on various machines and various materials. The deployment of the software using on-premises and cloud-based computational models drastically broadens the possible use of the software among various members of the AM supply chain.

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

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