Thermal image-driven thermal error modeling and compensation in CNC machine tools based on deep attentional residual network

Thermal error is a critical factor influencing the machining accuracy of CNC machine tools, so it is essential to comprehensively model and compensate for thermal errors in CNC machine tools. This paper proposes a deep attentional residual network thermal error prediction model driven by thermal image inputs. In contrast to traditional models that solely rely on temperature data, the proposed model utilizes thermal image data as a key input parameter and incorporates temperature data from sensitive points to fully represent the machine's temperature distribution. Furthermore, the attention mechanism is used to optimize the hyperparameters and network structure of the residual network model. Transfer learning is employed to improve training efficiency, reduce data requirements, and enhance the model's transferability. The optimized model achieves a prediction accuracy of 99.5% and converges more quickly. Finally, thermal error compensation experiments are conducted on the platform of the Siemens 840D system with an average effect of more than 70%. The proposed thermal error compensation method is effective and provides a foundation for precision machining.