Thermal error recognition and prediction method for economical motorized spindles based on thermal characteristics knowledge graph

Abstract

During CNC machining processes, economical motorized spindles exhibit nonlinear temperature increases and progressive thermal errors. Accurate recognition and prediction of these thermal errors are essential prerequisites for achieving stable and controllable machining processes. Existing thermal error models primarily rely on temperature signals while neglecting the coupling effects of other non-stationary, multi-source heterogeneous signals on thermal characteristics. This oversight results in limited theoretical understanding of the relationship between heat generation and multiple signal sources, leading to significant inaccuracies in thermal error recognition and prediction. To address these limitations, this paper elucidates the mechanism of motorized spindle thermal characteristics and their mapping relationships with multi-source signals, constructing them into interpretable thermal characteristic knowledge. Based on this foundation, a novel knowledge graph-based method for thermal error recognition and prediction is proposed, which effectively mitigates machining errors caused by thermal deformation. First, a knowledge graph is employed to structurally characterize the multi-source heterogeneous information of motorized spindles during the machining process, revealing the mapping mechanism of “machining process-multi-source signals-thermal characteristics-thermal errors”. The thermal characteristic knowledge is then systematically extracted through graph embedding. Second, a Long Short-Term Memory (LSTM) network with a flexible multi-head attention mechanism is developed, integrating graph embedding features and sensor data characteristics for thermal error recognition and prediction. Finally, the effectiveness and superiority of this method under different working conditions are verified on a high-speed CNC machine tool. The results demonstrate up to 36 % improvement in prediction accuracy compared to conventional machine learning methods.