Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
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Application of Multi-Layer Perceptron and Random Forest Method for Cylinder Plate Forming

Multi-Layer Perceptron과 Random Forest를 이용한 실린더 판재의 성형 조건 예측

  • Kim, Seong-Kyeom (Department of Naval Architecture and Ocean Engineering, INHA University) ;
  • Hwang, Se-Yun (Research Institute of Industrial Science and Technology, INHA University) ;
  • Lee, Jang-Hyun (Department of Naval Architecture and Ocean Engineering, INHA University)
  • 김성겸 (인하대학교 조선해양공학과) ;
  • 황세윤 (인하대학교 공과대학 산업과학기술 연구소) ;
  • 이장현 (인하대학교 조선해양공학과)
  • Received : 2019.12.27
  • Accepted : 2020.08.13
  • Published : 2020.10.20
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Abstract

In this study, the prediction method was reviewed to process a cylindrical plate forming using machine learning as a data-driven approach by roll bending equipment. The calculation of the forming variables was based on the analysis using the mechanical relationship between the material properties and the roll bending machine in the bending process. Then, by applying the finite element analysis method, the accuracy of the deformation prediction model was reviewed, and a large number data set was created to apply to machine learning using the finite element analysis model for deformation prediction. As a result of the application of the machine learning model, it was confirmed that the calculation is slightly higher than the linear regression method. Applicable results were confirmed through the machine learning method.

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References

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