신경망과 실험계획법을 이용한 절삭력 예측

Cutting foice signals are very useful to evaluate the cutting state, but many disturbing factors are occurring during cutting For the reliability of the analysis, selecting pure cutting foice signals from the original ones is needed In the curient study, using the ICA(Independent Component Analysis) effective cutting force components are seperated from the oliginal signals And using this, as input data of MLP(Multl-Layer Perception) cutting forces are predicted Experimental results are then compared with the predicted ones to verify the validation of the proposed model