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http://dx.doi.org/10.3744/SNAK.2020.57.5.297

Application of Multi-Layer Perceptron and Random Forest Method for Cylinder Plate Forming  

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)
Publication Information
Journal of the Society of Naval Architects of Korea / v.57, no.5, 2020 , pp. 297-304 More about this Journal
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.
Keywords
Roll bending; Machine learning; Multi-layer perceptron; Random forest; Finite Element Analysis(FEA); Center roller displacement;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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