Online prediction and control method for compensation regulation value in grinding processes

Abstract

In any grinding process, compensation regulation value is a crucial factor for maintaining precision during the batch processing of workpieces. Geometric characteristics, buffing allowance, temperature, wheel speed, and workpiece speed are the main factors that affect compensation regulation value in any grinding process. In this article, a novel prediction method for compensation regulation value is proposed based on incremental support vector machine and mixed kernel function. The support vectors for the prediction model are extracted using the convex hull vertex optimization algorithm, and the speed of the operation can be increased effectively. In addition, the parameters of the model are optimized using cross-validation optimization method to improve the accuracy of the prediction model. Then, the feedback control strategy of compensation regulation value for the grinding process is also proposed. Single-factor and multifactor experiments are implemented respectively using the proposed method. The results verify the feasibility and effectiveness of the proposed method. It is also noted that the machining accuracy is improved significantly in comparison with the machining without prediction and compensation control. Moreover, by applying the prediction compensation control of compensation regulation value to the active measurement and control of the grinding process, a feedback system is formed, and then the intelligentization of the grinding system can be realized.

Research on Fusion Predictive Control Method of Size and Roundness in Online Grinding

“…Through time series prediction, the prediction value can judge the error in advance and be transmitted to the machine tool in time through the measuring system, thus ensuring the effective processing. In papers [12]- [15], Zheng P et al use support vector machine to predict and analyze the shape and position error and other information in the machining process, demonstrating the feasibility of the application of prediction method in the machining process. In papers [16]- [22], the authors use neural network or other methods to predict and optimize the information of various parameters in the processing process.…”