Development of a Prediction System for 3D Printed Part Deformation

Authors

  • H. S. Park School of Mechanical and Automotive Engineering, University of Ulsan, Republic of Korea
  • N. H. Tran Faculty of Mechanical Engineering, University of Transport and Communications, Vietnam
  • V. T. Hoang Faculty of Information Technology, University of Transport and Communications, Vietnam
  • V. H. Bui Faculty of Mechanical Engineering, University of Transport and Communications, Vietnam
Volume: 12 | Issue: 6 | Pages: 9450-9457 | December 2022 | https://doi.org/10.48084/etasr.5257

Abstract

The Additive Manufacturing (AM) process is applied in industrial applications. However, quality issues of the printed parts, including part distortion and cracks caused by high temperature and fast cooling, result in high residual stress. The theoretical calculation equation shows elastic behavior which is the linear behavior between strain and stress. However, in practice with the additive manufacturing process, strain and stress have nonlinear behavior. So, the prediction of the deformation of a printed part is inaccurate. The contribution of this research is the creation of an Inherent Strain (IS)-based part deformation prediction method during the Selective Laser Melting (SLM) process. To have the deformation in the design stage, we developed software for calculating the IS value and predicting the deformation. The difference between the calculated results and the experimental results is still there, so, we proposed an algorithm and developed an optimization module for the system to minimize this difference. In the final optimal printing process, the parameters are derived in order for the real printing process to have the required quality of the SLM printed part.

Keywords:

selective laser melting, predicting deformation, inherent strain, heat treatment effect zone

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How to Cite

[1]
H. S. Park, N. H. Tran, V. T. Hoang, and V. H. Bui, “Development of a Prediction System for 3D Printed Part Deformation”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9450–9457, Dec. 2022.

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