• Title/Summary/Keyword: Weighter k-NN

Search Result 1, Processing Time 0.014 seconds

Machine Learning Model for Predicting the Residual Useful Lifetime of the CNC Milling Insert (공작기계의 절삭용 인서트의 잔여 유효 수명 예측 모형)

  • Won-Gun Choi;Heungseob Kim;Bong Jin Ko
    • Journal of Advanced Navigation Technology
    • /
    • v.27 no.1
    • /
    • pp.111-118
    • /
    • 2023
  • For the implementation of a smart factory, it is necessary to collect data by connecting various sensors and devices in the manufacturing environment and to diagnose or predict failures in production facilities through data analysis. In this paper, to predict the residual useful lifetime of milling insert used for machining products in CNC machine, weight k-NN algorithm, Decision Tree, SVR, XGBoost, Random forest, 1D-CNN, and frequency spectrum based on vibration signal are investigated. As the results of the paper, the frequency spectrum does not provide a reliable criterion for an accurate prediction of the residual useful lifetime of an insert. And the weighted k-nearest neighbor algorithm performed best with an MAE of 0.0013, MSE of 0.004, and RMSE of 0.0192. This is an error of 0.001 seconds of the remaining useful lifetime of the insert predicted by the weighted-nearest neighbor algorithm, and it is considered to be a level that can be applied to actual industrial sites.