Effect of Surface Finish and Post-Processing on the Fatigue Life of Additively Manufacturing Parts

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0828
Agency Tracking Number: N182-126-0206
Amount: $124,964.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N182-126
Solicitation Number: 2018.2
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2018-10-15
Award End Date (Contract End Date): 2019-04-18
Small Business Information
3190 Fairview Park Drive, Falls Church, VA, 22042
DUNS: 010983174
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Kelvin Leung
 (703) 226-4065
 kleung@tda-i.com
Business Contact
 Scott Bradfield
Phone: (703) 223-4061
Email: sbradfield@tda-i.com
Research Institution
N/A
Abstract
Additive manufacturing technology is becoming more popular for the fabrication of 3D metal products as it offers rapid prototyping and large design freedom. However, with more complex geometric features due to topology optimization, it becomes infeasible to carry out traditional surface machining process to improve surface roughness for fatigue performance. In this effort, we will develop a novel electropolishing process which is capable of accessing all open surfaces for complex part and critically examine its improvement on surface roughness and fatigue performance by comparing with traditional mechanical mass finishing. In order to simulate and optimize the electropolishing process, Integrated Computational Materials Engineering and machine learning approach will be utilized to link the process parameters with surface properties and fatigue performance. An artificial neural network will be implemented to relate electropolishing process parameters with polished surface roughness, while the Integrated Computational Materials Engineering toolset provides mechanistic modeling of surface roughness effects on fatigue life. Furthermore, a state-of-the-art hybrid optimization approach, combining sensitivity analysis, response surface method and genetic algorithm, is proposed to optimize process parameters to minimize surface roughness and maximize fatigue performance.

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

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