A variable modal decomposition and BiGRU-based tool wear monitoring method based on non-dominated sorting genetic algorithm II

In the metal cutting process, it is important to realize the effective monitoring of tool wear to ensure the quality of parts machining. A variable modal decomposition (VMD) and bi-directional gated recurrent neural network (BiGRU) based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for tool wear monitoring (TWM) problem. The method takes the envelope entropy and kurtosis of the decomposed signal as the objective function, and iteratively optimizes the number of decomposition layers k and the penalty factor α of the VMD by NSGA-II to obtain the optimal solution set. Meanwhile, an evaluation method of entropy weight- technique for order preference by similarity to ideal solution (EW-TOPSIS) is proposed to obtain the optimal parameter combinations in VMD. The VMD decomposed signals are screened by calculating the correlation coefficients, the time domain and frequency domain features of the screened signals are extracted, and the screened signals and features are mirrored. To realize the in-depth mining of the feature information of time series data, a BiGRU deep learning monitoring model is established. By comparing three classification methods, it has been proven that this method has achieved better results in monitoring tool wear state.