Thermal error modelling method of linear motor feed system for infrared thermal image

Thermal deformation caused by uneven heat accumulation in linear motor feed systems is a major contributor to positioning inaccuracies in precision machinery. Conventional models rely on discrete temperature sensors, failing to fully capture temperature field distribution and highly nonlinear thermal-mechanical coupling, resulting in insufficient robustness under complex conditions. This study proposes an infrared thermal image-driven thermal error modelling method, systematically validating its advantages over discrete sensor-based schemes. Infrared thermal images and thermal error data under multiple load conditions were collected to construct a temperature-field-aware dataset. An enhanced ResNet18 architecture was designed, reconstructing the traditional cascaded CBAM into a parallel channel-spatial attention mechanism to synchronously select multi-dimensional features, improving sensitivity to local thermal gradients and model stability. A regression layer was added for continuous error estimation, combined with Dropout to mitigate overfitting. Experiments show the model achieves over 0.95 prediction accuracy (R2), with MAE and RMSE both below 0.5 μm. Compared with discrete sensor models and baseline CNNs, it reduces RMSE by 18.3% and improves stability by 12.5%, breaking through the bottlenecks of discrete sensors. It provides reliable support for real-time thermal error compensation in high-precision feed systems, with industrial deployment potential.