Thermal error compensation method for CNC machine tools based on deep convolution neural network

Abstract

To avoid the resetting of temperature-sensitive points caused by seasonal and weather changes and the influence on the accuracy of the thermal error model, a thermal error prediction and compensation method for CNC machine tools without preselected temperature-sensitive points was proposed based on deep learning method. The original temperature data of the CNC machine tool were converted into thermal images and directly serve as the input of the deep learning network. To improve the prediction accuracy of the thermal error model, a thermal error model of CNC machine tool with 10 hidden layers was constructed by using the powerful image feature learning ability of deep convolution network. The nonlinear mapping relationship between temperature image and thermal error without pre-selection of temperature key points was established, and more relationships between thermal error and temperature feature of a machine tool were retained. STM32F4 microprocessor was used as thermal error compensation controller to develop software and hardware system. The application experiment of the thermal error compensation system is carried out on the CNC grinder, and the effectiveness of the thermal error compensation system is verified.