S2S-LSTM深度學習技術於鈦及鋁合金鏡面切削之PCD銑刀剩餘壽命預測

Both the titanium alloy and aluminum alloy cutting by using Polycrystalline Diamond (PCD) milling cutter for obtaining mirror milling surface results are important processing technologies in the industry. To improve the production efficiency or enhance the cutting performance of this cutting technology, the Remaining Useful Life (RUL) prediction of PCD milling cutter becomes one of the major issues nowadays. The Sequence to Sequence Long Short-term Memory (S2S-LSTM) is used in this research as the prediction model to carry out PCD milling cutter's RUL prediction, and two times of PCD milling cutting experiments for titanium and aluminum alloy are designed and carried out. In the experiments, the data of the vibration signal, sound signal, and the surface roughnesses of the workpieces are measured and used as the datasets. The prediction model yielded F1-scores of 98.1% and 95.8% by using the validation datasets of the two experiments. The proposed model is also compared with other AI (Artificial Intelligent) models, such as RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), and LSTM under the same batch size, epoch, learning rate, and other hyper-parameters.