During the part forming in laser powder bed fusion process, thermal distortion is one big problem due to the thermal stress which is caused by the high cooling rate and temperature gradient. Therefore, it is important to know the effect of process parameters on thermal and stress evolution in the melt zone. In this paper, a 3D finite element model for Selective Laser Melting (SLM) process based on sequentially coupled thermo-mechanical field analysis was developed for accurately predicting thermal history and surface features, like distortion and residual stress. Temperature dependent material properties for performed material 304L stainless steel are incorporated into the model capturing the change from powder to fully dense solid stainless steel. Surface temperature gradients and thermal stress were fully presented in the development of different parameter sets, which designed for the probability of reducing defect formation. Simulation results showed that the sequent thermal cyclic melting in successive scanned tracks resulted in alternating compressive and tensile thermal stresses. A predictive model for thermal and stress field in large part by selective laser melting process is come up in Part II. After the parts cooled down to room temperature, higher residual stresses were found in longitudinal stress. This paper will provide guidance on how to achieve minimum residual stresses and deformations by the study of the process parameters.

Meeting Name

25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, ICPR 2019 (2020: Aug. 9-14, Chicago, IL)


Mechanical and Aerospace Engineering

Research Center/Lab(s)

Center for Research in Energy and Environment (CREE)

Keywords and Phrases

Distortion; Finite element analysis; Residual stress; SLM

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2019 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publication Date

01 Aug 2019