Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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A Machine Learning Program for Impact Fracture Analysis

머신러닝을 이용한 충격파면 해석에 관한 연구

  • Lee, Seung-Jin (Graduate Mechanical Engineering, Kumoh National Institute of Technology) ;
  • Kim, Gi-Man (Dept. Mechanical System Engineering, Kumoh National Institute of Technology) ;
  • Choi, Seong-Dae (Dept. Mechanical System Engineering, Kumoh National Institute of Technology)
  • 이승진 (금오공과대학교 대학원 기계공학과) ;
  • 김기만 (금오공과대학교 기계시스템공학과) ;
  • 최성대 (금오공과대학교 기계시스템공학과)
  • Received : 2020.11.30
  • Accepted : 2020.12.15
  • Published : 2021.01.31
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Abstract

Analysis of the fracture surface is one of the most important methods for determining the cause of equipment structural failure. Whether structural failure is caused by impact or fatigue is necessary information in industrial fields. For ferrous and non-ferrous metal materials, two fracture phenomena are generated on the fracture surface: ductile and brittle fractures. In this study, machine learning predicts whether the fracture is based on ductile or brittle when structurural failure is caused by impact. The K-means algorithm calculates this ratio by clustering the brittle and ductile fracture data from a photograph of the impact fracture surface, unlike the existing method, which calculates the fracture surface ratio by comparison with the grid type or the reference fracture surface shape.

Keywords

References

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