Online recognition of contour error of diamond roller

Abstract

Objectives: With the development of the manufacturing industry, precision grinding has become an indispensable part of the high-end manufacturing field. As a key tool for precision grinding, the surface reshaping technology of diamond rollers is one of the critical technologies for making diamond rollers. Currently, the diamond grinding wheel grinding method is commonly used for diamond roller precision reshaping. In the process of precision reshaping, the surface profile of the diamond roller is an important indicator for assessing its reshaping quality. However, in the current reshaping detection, the workers often stop the machine to remove the rollers and place them on the profiler, which greatly increases the time and the cost of roller reshaping. Therefore, to improve the efficiency of diamond roller reshaping, a method based on sensors to monitor the state of contour reshaping during the process of diamond roller reshaping is explored. Methods: To realize online monitoring and discrimination of the diamond roller contour reshaping state, the diamond roller grinding monitoring platform was first built according to the structure of the GC-X3 machine tool, and LabVIEW was used to read and save the signals collected by the acquisition card. Secondly, the diamond rollers with different contour reshaping states were processed by the GC-X3 machine tool, and the signals with different reshaping states collected by the computer were analyzed in time and frequency domains. The wavelet packet decomposition method was used to decompose the signals according to the characteristics of time and frequency domains, and the signals of different nodes obtained after decomposition were calculated and compared to identify and determine the characteristic values of the signals. Finally, the machine learning method was used to establish the recognition model of the diamond roller contour reshaping state. The model is based on the mapping relationship between the characteristic values of the vibration signals and the contour reshaping state to realize the intelligent recognition of the contour reshaping state of the diamond roller. Results: After preprocessing the signal, it can be observed through the time and the frequency domains that the maximum amplitudes of the X and Y axis vibration signals decrease with the improvement of the roller profile accuracy. The amplitudes of the X and Y axis vibration signals during rough trimming are 1.00 and 0.50 V, respectively. The amplitudes of the X and Y axis vibration signals during fine trimming are 0.50 and 0.40 V, respectively. The amplitudes of X and Y axis vibration signals at the completion of trimming are 0.29 and 0.27 V respectively, and the main frequencies of signals at the completion of rough, fine, and finishing are all distributed within 1 000 Hz. The root mean square and the variance of wavelet packet coefficients mainly occur at nodes (3,0) to (3,3), and as the grinding accuracy increases, the root mean square and the variance of wavelet packet coefficients decrease gradually. When rough trimming diamond rollers with diamond grinding wheels, the roundness error value of the roller profile is larger than that after fine trimming and the completion of trimming, resulting in a larger local grinding allowance. A large number of abrasive particles from the diamond grinding wheel come into contact with the roller, which increases the material removal amount of the diamond roller, the grinding force also increases, and the resulting vibration also increases accordingly. During the fine trimming process, the roundness error of the roller contour is reduced, so that the diamond wheel cutting the diamond roller allowance is reduced, the removal amount is also reduced, the grinding force is reduced, and the vibration generated is also reduced. At the same time, the root mean square and the variance of the wavelet packet coefficients in the completion of trimming decrease correspondingly as the roundness error value of the roller profile decreases. As the trend and fluctuation of the root mean square and the variance of the wavelet packet coefficients in the X and Y axes are generally consistent, the root mean square and the variance of the wavelet packet coefficients in the direction of the X-axis are selected as the feature vectors for the classification of experimental data. The wavelet packet is used to decompose the X-axis signal, and the variance and the root mean square of each node are calculated to obtain 16 characteristic values of the vibration signal. There is a mapping relationship between these characteristic values and the reshaping state of the roller, and the random forest algorithm is used to classify the vibration signals based on these 16 characteristic values, and the recognition accuracy reaches 93.3%. Conclusions: The contour reshaping of the diamond roller is the key to the production of diamond rollers. In the process of roller machining, the method combining sensor monitoring and machine learning models is used to judge the reshaping state of the diamond roller. The method can be used to identify the modified state of the diamond roller profile, reduce the number of roller disassemblies and roller profile detections, and improve the processing efficiency.

Keywords

• diamond roller
• vibration signal
• wavelet packet coefficients
• online recognition
• random forest