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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: N68335-18-C-0084
Agency Tracking Number: N16A-022-0121
Amount: $2,279,683.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N16A-T022
Solicitation Number: 16.A
Solicitation Year: 2016
Award Year: 2018
Award Start Date (Proposal Award Date): 2017-11-14
Award End Date (Contract End Date): 2022-09-09
Small Business Information
335 Madison Avenue, 3rd Fl
New York, NY 10017-0000
United States
DUNS: 080153545
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Annie Wang
 (267) 241-1119
Business Contact
 Zachary Simkin
Phone: (914) 420-4236
Research Institution
 The Pennsylvania State University
 Jennifer Lear Jennifer Lear
110 Technology Center Building
University Park, PA 16802-0000
United States

 (814) 865-7650
 Nonprofit College or University

The objective in this project is to implement and validate a probabilistic qualification framework that will enable additive manufacturing (AM) materials and part qualification through the use of a data-driven predictive model within a statistical framework. Senvol seeks to develop and validate a data-driven ICME probabilistic framework for assisting qualification of AM materials and parts. Phase II focuses on 4 main Thrust Areas: (1) Validation of the ICME probabilistic framework, (2) Extension quantification capability, (3) Data collection protocol development, and (4) Software improvement. The objective of the Phase II Base is to validate the probabilistic framework through a rigorous experimental program on a representative structural component, and implement a capability that would quantify the accuracy of extending previously trained ICME predictive models for use in predicting the ICME relationships of a new dataset. In the Phase II Option, the project team plans on demonstrating and validating an approach for using the predictive model, capabilities, and data collection protocol for the case where a new dataset is no longer accurately described by a previously trained model. This demonstration will show how to gather additional test data to re-qualify the updated process.

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

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