## 1 Introduction
Another idea is time-frequency decomposition methods such as short-time Fourier transform and wavelet transform to directly decompose the signal into a two-dimensional time-frequency plane, obtain the time-frequency diagram of the signal, and then extract the features reflecting the time-frequency characteristics of the signal from the time-frequency diagram. [PERSON] et al. [16] proposed a method based on time-frequency image feature extraction. The time-frequency image of the signal was obtained by wavelet transform, and then the non-subsampling contourlet transform was combined with the local binary mode (LBP) frequency image-related features. In terms of signal preprocessing and feature extraction based on milling signals, the time-domain and frequency-domain feature-extraction methods are obviously not suitable for signal analysis with highly non-stationary characteristics in the cutting of aeronautical structural parts, and the existing time-frequency feature extraction methods mainly focusing on the frequency band energy features based on signal decomposition or the time-frequency diagram, the sensitive features in the time-frequency diagram cannot be effectively extracted, and the time-frequency diagram cannot reflect the time-varying characteristics of the instantaneous frequency components of the signal due to the influence of cutting energy. Therefore, the mining of information in the signal is not comprehensive. On the other hand, the online monitoring of milling is essentially a pattern recognition problem, which can be divided into supervised or unsupervised pattern recognition according to whether the training process of the model requires teacher data. Supervised pattern recognition requires sample labels prior to use, which together with monitoring signals form teacher data to train model parameters. At present, the supervised methodsused for tool wear monitoring mainly include: SVM, PNN, and HMM [17; 18; 19; 20; 21]. The high manufacturing cost of aeronautical structural parts, the large size of the parts, and the fast tool wear make it difficult for supervised pattern recognition to obtain sufficient training samples, making it difficult for practical application promotion. The biggest feature of unsupervised pattern recognition is that model parameters can be established only by monitoring signals [22]. In this paper, a cutting condition independent tool wear monitoring system based on unsupervised corner milling is studied. Firstly, feature extraction based on time-frequency image and feature selection based on trend item analysis was proposed to obtain acoustic emission signal (AE) features that are sensitive to tool wear conditions and insensitive to cutting conditions. Secondly, clustering based on adjacent grids searching (CAGS) was proposed to analyze the obtained data. Thus, different tool wear conditions were identified without test data. Finally, the density factor was proposed to characterize the tool wear values corresponding to different clustering results. In order to verify the effectiveness of the proposed tool condition monitoring (TCM) system, a series of experiments were conducted. Meanwhile, the obtained results were compared with those with the state of art of supervised methods. Results showed the effective feature extraction for tool wear conditions in corner-milling process could be realized through time-frequency image. In addition, the cost of the monitoring system was greatly reduced by using the unsupervised pattern recognition method.