Dimensional Error Compensation Based on Data-Driven Sliding Mode Terminal Iterative Learning Control for CNC Batch Grinding

Abstract

Dimensional error of workpiece is an important index that indicts the grinding accuracy of machine tools. Conventional error compensation strategies focus on modeling one or several error factors for a particular CNC machining type, which are time consuming and costly. Based on data-driven sliding mode terminal iterative learning control, a new dimensional error compensation method is proposed in this paper. The compensation algorithm based on an iterative sliding surface and an objective function is established using the terminal experimental data. A modified NC program is fed to the machine tool to push the workpiece dimension towards desired. The theoretical analysis and experimental results show the proposed compensation method can effectively improve the batch grinding accuracy of the machine tool, which shows good perspective in manufacturing industry.