Journal of Practical Engineering Education (실천공학교육논문지)

Korean Institute for Pratical Engineering Education (한국실천공학교육학회)
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Determination of Optimal Adhesion Conditions for FDM Type 3D Printer Using Machine Learning

  • Woo Young Lee (School of Mechanical Engineering, Korea University of Technology and Education) ;
  • Jong-Hyeok Yu (FMSolution) ;
  • Kug Weon Kim (Department of Mechanical Engineering, Soon Chun Hyang University)
  • Received : 2023.07.07
  • Accepted : 2023.08.18
  • Published : 2023.08.30
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Abstract

In this study, optimal adhesion conditions to alleviate defects caused by heat shrinkage with FDM type 3D printers with machine learning are researched. Machine learning is one of the "statistical methods of extracting the law from data" and can be classified as supervised learning, unsupervised learning and reinforcement learning. Among them, a function model for adhesion between the bed and the output is presented using supervised learning specialized for optimization, which can be expected to reduce output defects with FDM type 3D printers by deriving conditions for optimum adhesion between the bed and the output. Machine learning codes prepared using Python generate a function model that predicts the effect of operating variables on adhesion using data obtained through adhesion testing. The adhesion prediction data and verification data have been shown to be very consistent, and the potential of this method is explained by conclusions.

Keywords

Acknowledgement

This paper was researched by the 2023 Korea University of Technology and Education Professor's Education Research Promotion Project.

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