- Volume 22 Issue 5
DOI QR Code
A study on the optimum cutter spacing ratio according to penetration depth using decision tree-based and SVM regressions
의사결정나무 기반 회귀분석과 SVM 회귀분석을 이용한 커터 관입깊이에 따른 최적 커터간격 비 연구
- Lee, Gi-Jun (Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST)) ;
- Ryu, Hee-Hwan (Korea Electric Power Research Institute (KEPRI)) ;
- Kwon, Tae-Hyuk (Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST))
- Received : 2020.07.17
- Accepted : 2020.08.31
- Published : 2020.09.30
Cutter cutting tests for the cutter placement in the cutter head are being conducted through various studies. Although the cutter spacing at the minimum specific energy is mainly reflected in the cutter head design, since the optimum cutter spacing at the same cutter penetration depth varies depending on the rock conditions, studies on deciding the optimum cutter spacing should be actively conducted. The machine learning techniques such as the decision tree-based regression model and the SVM regression model were applied to predict the optimum cutter spacing ratio for the nonlinear relationship between cutter penetration depth and cutter spacing. Since the decision tree-based methods are greatly influenced by the number of data, SVM regression predicted optimum cutter spacing ratio according to the penetration depth more accurately and it is judged that the SVM regression will be effectively used to decide the cutter spacing when designing the cutter head if a large amount of data of the optimum cutter spacing ratio according to the penetration depth is accumulated.
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