Design & Manufacturing

Korean Society of Die & Mold Engineering (한국금형공학회)
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A study on computational analysis modeling for evaluating the performance of additive manufacturing specimen

적층제조시편 성능 평가를 위한 전산해석 모델링 도출에 관한 연구

  • Yeo-Ul Song (Dept. of Industrial Machinery DX, Korea Inst. of Machinery & Materials) ;
  • Pil-Ho Lee (Dept. of 3D Printing, Korea Inst. of Machinery & Materials) ;
  • Dong-Woon Shin (Dept. of 3D Printing, Korea Inst. of Machinery & Materials) ;
  • Gyeong-Yun Baek (Dept. of Convergence Mechanical Eng., Gwangju Univ.)
  • 송여울 (한국기계연구원 산업기계DX연구실) ;
  • 이필호 (한국기계연구원 3D프린팅연구실) ;
  • 신동운 (한국기계연구원 3D프린팅연구실) ;
  • 백경윤 (광주대학교 융합기계공학과)
  • Received : 2024.01.09
  • Accepted : 2024.03.31
  • Published : 2024.03.31
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Abstract

Additive manufacturing grants control over both the shape and properties of the product. Therefore, validating product operation necessitates predicting its properties. In this study, an optical fiber-based temperature sensor was inserted into an additively manufactured specimen, and the actual temperature was collected. A machine learning model was constructed using the collected temperature data for calibration, enabling accurate prediction of physical properties. These predicted properties were then integrated into structural analysis to assess the performance of the specimen.

Keywords

Acknowledgement

본 연구는 한국기계연구원 기관 기본사업(NK248I)과 한국연구재단의 한·중 산·학·연 대형공동연구(NRF-2022K1A3A1A61015007)의 지원을 받아 연구되었음.

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