Identification of a Workpiece Temperature Compensation Model for Automatic Correction of the Cutting Process

Abstract

This article describes a system for measuring and compensating for errors resulting from the cutting process in order to improve the accuracy of the workpiece. Measurements were performed by means of an automatic measurement unit. The diameter of the workpiece was measured at two points, and at the same time, the temperature at the end face of the workpiece was measured. These measurements were used in Statistical Process Control (SPC). Based on the measured values, the process stability was checked and an error correction value was determined for the next item. Moreover, the value of the correction was influenced by the assumed value of tool wear, in accordance with the adopted model, and the possibility of achieving the assumed surface quality. The diameter of the workpiece for SPC purposes was measured under industrial conditions using an automatic measurement unit, which indicates that the temperature of the workpiece during the measurement was significantly higher than the reference temperature. The study focuses on the possibility of identifying a workpiece temperature compensation model in measurements of the workpiece diameter for the purpose of introducing an additional change in the correction value. It was found that a model with a constant correction value and a linear model poorly reflect the nature of the changes. On the other hand, the Autoregressive with Extra Input (ARX) model and the Nonlinear Autoregressive with Extra Input (NLARX) model, with a neural network, are able to map the inertia of the system and map the process with greater accuracy. In this way, measurements performed in industrial conditions can more accurately determine the possibility of achieving the assumed tolerance of the finished product. At the same time, the research shows that the temperature compensation model is nonlinear, and that the maximum possible machining accuracy of the workpiece can be achieved thanks to the repeatable measurement and compensation technique.