Abstract
Finite Element Analysis (FEA) is used to predict the transient thermal cycle and optimize process parameters to analyze these effects on deformation and residual stresses. However, the process of predicting the thermal history in this process with the FEA method is usually time-consuming, especially for large-scale parts. In this paper, an effective predictive model of part deformation and residual stress was developed for accurately predicting deformation and residual stresses in large-scale parts. An equivalent body heat flux proposed from the single layer laser scan model was imported as the thermal load to the layer by layer model. The hatched layer is then heated up by the equivalent body heat flux and used as a basic unit element to build up the macroscale part. The thermal history and residual stress fields of two solid parts with different support structures during the SLM process were simulated. Layer heat source method has the capability for fast temperature prediction in the SLM process, while sacrificing modeling details for the computational time-saving purpose. Thus numerical modeling in this work can be a very useful tool for the parametric study of process parameters, residual stresses and deformations.
Recommended Citation
L. Li et al., "Predictive Model for Thermal and Stress Field in Selective Laser Melting Process -- Part II," Procedia Manufacturing, vol. 39, pp. 547 - 555, Elsevier B.V., Aug 2019.
The definitive version is available at https://doi.org/10.1016/j.promfg.2020.01.416
Meeting Name
25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, ICPR 2019 (2020: Aug. 9-14, Chicago, IL)
Department(s)
Mechanical and Aerospace Engineering
Research Center/Lab(s)
Center for Research in Energy and Environment (CREE)
Keywords and Phrases
Distortion; Finite element analysis; Layer heat source model; Residual stress; SLM
International Standard Serial Number (ISSN)
2351-9789
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2019 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Aug 2019
