A Conceptual Design of Residual Stress Reduction with Multiple Shape Laser Beams In Direct Laser Deposition
Residual stress is a major problem in metal parts fabrication with the direct laser deposition (DLD) process due to severe temperature gradient around a molten pool. A three-dimensional finite element analysis (FEA) model with a simplified substrate clamping fixture modeling method is proposed, validated, and then implemented with a novel DLD heat input strategy in Ti-6Al-4V thin-wall structure fabrication, which was applied with multiple beam shapes, including a super-Gaussian beam, Gaussian beam, and inverse-Gaussian beam, to reduce residual stress in the final part. A regression model of the heat input and final part residual stress was obtained via a three-factor two-level full factorial design. An optimized heat input strategy was achieved based on response surface contour plots of the regression model.
L. Yan et al., "A Conceptual Design of Residual Stress Reduction with Multiple Shape Laser Beams In Direct Laser Deposition," Finite Elements in Analysis and Design, vol. 144, pp. 30 - 37, Elsevier, May 2018.
The definitive version is available at https://doi.org/10.1016/j.finel.2018.02.004
Mechanical and Aerospace Engineering
Intelligent Systems Center
Keywords and Phrases
Aluminum; Aluminum alloys; Conceptual design; Deposition; Finite element method; Gaussian distribution; Laser beams; Regression analysis; Residual stresses; Substrates; Ternary alloys; Titanium; Titanium alloys; Direct laser deposition; Inverse gaussian; Metal parts fabrications; Regression model; Residual stress reductions; Super-Gaussian; Three dimensional finite element analysis; Ti-6 Al-4 V; Gaussian beams; Finite element analysis; Gaussian beam; Inverse-Gaussian beam; Super-Gaussian beam; Ti-6Al-4V
International Standard Serial Number (ISSN)
Article - Journal
© 2018 Elsevier, All rights reserved.
01 May 2018
The authors appreciate the sponsorship by Boeing through the Center for Aerospace Manufacturing Technologies (CAMT), National Science Foundation Grant # CMMI-1547042, and the Intelligent Systems Center (ISC) at Missouri S&T.