Exogenous input autoregressive model optimization based on mixed variables for offline prediction CNC Swiss lathes thermal errors

Abstract

Accurate prediction models of thermal errors are very useful for improving the machining accuracy of machine tools; it is also the core of thermal error compensation technology. Often, it is preferable to predict thermal deformation using the exogenous input autoregressive model, as opposed to computational inaccuracy and non-robustness existing in the static model. However, the autoregressive model needs to measure the thermal error online, which can be intrusive to the production process and reduce production efficiency. Previously, applying models to solving the engineering problem of machine thermal errors requires balancing accuracy and productivity. To simultaneously ensure prediction accuracy, robustness and productivity, this paper presents a new exogenous input autoregressive modeling approach based on mixed variables (MV-ARX) in CNC Swiss lathes. In addition, offline prediction is achieved by replacing online measurements with estimates of thermal errors. The effects of factors on thermal error, such as ambient temperature and spindle speed, are analyzed through thermal characteristic experiments. The K-means clustering method was used to select the thermal critical point, and the exogenous input autoregressive prediction model was optimized by combining the selected temperature variables with the spindle speed to improve the accuracy and robustness of offline prediction. The effectiveness of the proposed model is verified by comparing it with conventional models based on temperature-variable autoregression (TV-ARX) and multiple linear regression (MLR).