You are here

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-0221
Agency Tracking Number: N16A-004-0029
Amount: $79,937.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N16A-T004
Solicitation Number: 2016.0
Timeline
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
3190 Fairview Park Drive
Falls Church, VA 22042
United States
DUNS: 010983174
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Azadeh Keshtgar
 (703) 226-4071
 akeshtgar@tda-i.com
Business Contact
 Scott Bradfield
Phone: (703) 226-4061
Email: sbradfield@tda-i.com
Research Institution
 Lawrence Livermore National Laboratory
 Scott Tyler
 
7000 East Ave.
Livermore, CA 94550
United States

 () -
 Federally Funded R&D Center (FFRDC)
Abstract

TDA has teamed up with Lawrence Livermore National Laboratory as its research institution collaborator to address the target STTR topic objective of quantifying the uncertainties in the mechanical behavior of the AM parts. To quantify uncertainties by minimizing both the computational burden and expensive testing and also overcoming the IP concerns, we propose a novel approach with three layered interconnected framework to include: fast simulations scaling between local model and full scale model, adaptive surrogate modeling using dimensionless variables, and critical supporting experiments.During Phase I efforts, TDA plans to develop a fully automated thermal-mechanical finite element numerical simulation tool to predict AM part intrinsic properties for variable input parameters. We follow an ICME framework for multi-scale AM simulation module consisting of a coarse-grain module for component level processing and a high fidelity module capturing material local behavior. We propose using an innovative data-driven stochastic framework to characterize the effect of material and process uncertainties on the mechanical performance of additively manufactured parts by focusing on Selective Laser Melting process (SLM) and Titanium alloy. We propose analytical approaches to predict of mechanical performance and also quantify uncertainty in the selected few mechanical performance parameters using a novel surrogate modeling technique.

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

US Flag An Official Website of the United States Government