Over the years, machine tool manufacturers have moved steadily towards the enhancement of machining accuracy to improve the quality of finished products. In this study, the thermal deformation of a machine spindle, which has a profound effect on machining accuracy, was investigated. The temperatures of the front and rear spindle bearings, and of the environment as well as the Z-axis displacement on a model MC4200BL CNC lathe (Hybrid Sphere) were measured under long-term operating conditions. Measurements were carried out at spindle speeds of 1000, 1500, 2000, 2500, and 3000 rpm, and the data were used to establish a model for the prediction of spindle displacement. A back propagation neural network (BPNN) was used to establish the model and explore adjustments of the training function, the data training ratio, and the number of neurons in the hidden layer. Results of the experiments showed that the coefficient of determination (R 2) of the prediction model derived from the best parameters can be up to 0.9948. This was much better than the 0.8273 achieved by the partial least squares regression method.
When current technology keeps advancing, global machine tool manufacturers are gradually moving toward smart production lines. The ball bearing is an important fixed part of a rotating shaft; its key function is to bear the load acting on the shaft and maintain the center position of the shaft. If the bearing is damaged, there will be abnormal vibration, runout, and abnormal noise. Hence, the fault detection and recognition of the ball bearing are particularly important. The fault signal data of the ball bearing used in this study are obtained from the Case Western Reserve University (CWRU), and we establish a ball bearing status recognition model according to different signal-captured positions. First, the infinite impulse response (IIR) filter and approximate entropy (ApEn) are used to extract the features of the signals. Afterwards, the data extracted from the features are used for model establishment and training through a back propagation neural network (BPNN) and a support vector machine (SVM). In general, the SVM classification is better than the BPNN, but through a series of experimental methods, we confirmed that the optimal BPNN parameters of this sample, including training function, data training ratio, and the number of neurons, make the recognition rate of the BPNN higher than that of the general SVM, and the accuracy rate reaches 95%.
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