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Integrated Computational Material Engineering Approach to Additive Manufacturing for Stainless Steel (316L)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-16-P-2073
Agency Tracking Number: N16A-022-0073
Amount: $79,961.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N16A-T022
Solicitation Number: 2016.0
Solicitation Year: 2016
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-07-11
Award End Date (Contract End Date): 2017-05-10
Small Business Information
2545 Farmers Drive Suite 200
Columbus, OH 43235
United States
DUNS: 789156841
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Wei-Tsu Wu
 (614) 451-8322
Business Contact
 Juipeng Tang
Phone: (614) 451-8320
Research Institution
 The Ohio State University
 Dr. Wei Zhang
1248 Arthur Adams Drive \N
Columbus, OH 43221
United States

 (614) 292-0522
 Nonprofit College or University

We are proposing to identify an ICME architecture that will enable the multi-scale modeling of additive manufacturing (AM) process at both the component level as well as at the meso-scale level such that the final part quality and performance can be predicted accurately. At the component level, the proposed ICME framework would help in predicting residual stresses, distortion and the necessary support fixtures needed to minimize distortion, while considering optimal build conditions such as laser energy, the laser path and other relevant processing conditions. At the meso-scale level, the objective of the proposed ICME framework is to identify a computationally efficient methodology to predict local temperature distribution, molten pool shape, porosity and other relevant microstructural features. It is envisioned that the proposed ICME architecture would support surrogate models such as phenomenological models that can predict microstructural features as a function of processing parameters. By extension, the same ICME framework should be able to support surrogate microstructure to property models using either Neural network models or Bayesian models. Existing sensitivity analysis and probabilistic modeling techniques along with uncertainty quantification methods can be extended to model AM processes which would help in rapid qualification of additive manufacturing process and parts.

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

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