Temperature Sensing and Two-stage Integrated Modeling of the Thermal Error for a Computer-numerical Control Swiss-type Turning Center

Abstract

For tool machinery, the most crucial factor affecting the machining precision is thermal deformation. Thus far, the most popular method of reducing thermal deformation has been considered as the compensation method, and many mathematical compensation methods have been proposed. However, attempts to develop a more comprehensive model are continuing. To improve the prediction accuracy, in this study, we propose a two-stage integrated data-mining scheme. The first stage, using rough set theory, focuses on how to manipulate the measured problem-dependent temperature and deformation data. The second stage, using a deep-learning neural network scheme, models the relationship between the temperature increase and the thermal error. Comparisons of the proposed method with other methods are also made. Results show that marked improvements are obtained using our proposed integrated data mining scheme.

Recent Advances on Machine Learning Applications in Machining Processes

“…Authors have reduced the detected error under 10 µm. Wang et al [68] used DL applications for thermal deformation modeling using data mining based on RST and reducing the thermal error of~99%. Fujishima et al [69] focused on thermal displacement prediction, applying a DNN with Bayesian Dropout and considering the sensor failures to test the robustness of the model.…”

An Axial Attention-BiLSTM-based method for predicting the migration of CNC machine tool spindle thermal error under varying working conditions

“…Makoto Fujishima et al [16] evaluated the accuracy of thermal error prediction based on deep learning adaptive adjustment of compensation weights. Wang et al [17] used rough set theory to pre-process temperature and thermal error data, then applied deep learning neural network to build thermal error models.…”