Continuous tool wear prediction based on Gaussian mixture regression model

The prediction of continuous tool wear process plays an important role in realizing adaptive control and optimizing manufacturing process so as to improve production efficiency and quality of the workpiece. However, the complexity of the tool wear process and the unpredictable disturbance during milling process make it difficult to realize robust and accurate estimation of the tool wear value. In this paper, the Gaussian mixture regression (GMR) model is proposed to realize continuous tool wear prediction based on features extracted from cutting force signal. The main characteristic of the GMR model is that the relationship between the tool wear value and the features is built by the combination of the Gaussian mixture model in which the variation of the training data is described by the probability density of the Gaussian distribution, and the wild data can be abandoned if its probability is small enough. To test the effectiveness of the proposed method, the experiment of titanium alloy milling was carried out, and the spectrum peak value corresponding to the harmonic of tooth passing frequency was extracted as the explanatory variables to predict the tool wear value. In addition, multiple linear regression, radius basis function, and back propagation neural network are also adopted to make a comparison with the GMR model. The analysis of four performance criteria shows that the GMR-based method is the most accurate among these methods.