Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM

Machined surface quality and dimensional accuracy are significantly affected by tool wear in machining process. Tool wear state (TWS) recognition is highly desirable to realize automated machining process. In order to improve the accuracy of TWS recognition, this research develops a TWS recognition scheme using an indirect measurement method which selects signal features that are strongly correlated with tool wear to recognize TWS. Firstly, three time domain features are proposed, including dynamic time warping feature and two entropy features. The time, frequency, and time-frequency domain features of the vibration and force signals are extracted to form a feature set. Secondly, gradient boosting decision tree (GBDT) is adopted to select the optimal feature subset. Lastly, contrastive divergence (CD) and RMSspectral are used to train hybrid classification RBM (H-ClassRBM). The trained H-ClassRBM is used for TWS recognition. The PHM challenge 2010 data set is used to validate the proposed scheme. Experimental results show that the proposed features have better monotonicity and correlation than the classical features. Compared with CD and Adadelta, CD and Adagrad, and CD and stochastic gradient descent with momentum, the H-ClassRBM trained by CD and RMSspectral improves recognition accuracy by 1%, 2%, and 2%, respectively. Compared with feedforward neural network, probabilistic neural network, Gaussian kernel support vector machine, and H-ClassRBM, the proposed TWS recognition scheme improves recognition accuracy by 37%, 51%, 9%, and 8%, respectively. Therefore, the proposed TWS recognition scheme is beneficial in improving the recognition accuracy of TWS, and provides an effective guide for decision-making in the machining process.