An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

This study explores the application of artificial intelligence in lathe cutting machine tools in smart manufacturing. Long-term processing will cause thermal deformation of the lathe cutting tool machine, which will cause displacement errors of the cutting head and damage to the final product. Using time-series thermal compensation, the research develops a predictive system that can be applied in industry using edge computing technology to predict the thermal displacement of machine tools. The study conducted two experiments to optimize the temperature prediction model and predict the five-axis displacement of the temperature point. Furthermore, a genetic algorithm is used to optimize the LSTM model to predict the thermal displacement of the machine tool. The results show that the GA-LSTM model achieved a thermal displacement prediction accuracy of 0.99, while the average accuracy of the LSTM, GRU, and XGBoost models was 0.97. Based on the analysis of training time and model accuracy, the study recommends using LSTM, GRU, and XGBoost models to design and apply to systems that use edge devices such as Raspberry Pi for thermal compensation.