Application of ACO-BPN to thermal error modeling of NC machine tool

Thermal errors are the major contributor to the dimensional errors of a workpiece in precision machining. Error compensation technique is a cost-effective way to reduce thermal errors. Accurate modeling of errors is a prerequisite of error compensation. In this paper, four key temperature points of a NC machine tool were obtained based on clustering method. A thermal error model based on the four key temperature points was proposed by using ant colony algorithm-based back propagation neural network (ACO-BPN). The ACO-BPN method improves the prediction accuracy of thermal deformation in the NC machine tool. A thermal error compensation system was developed based on the proposed model, and which has been applied to the NC machine tool in daily production. The results show that the thermal drift in workpiece diameter has been reduced from 33 to 8 μm from its center of tolerance.