Abstract
A two-axis differential micro-feed system (TDMS) can overcome the accuracy limitation of the conventional drive feed system (CDFS). However, the heat induced by friction in the ball screw, bearings, and motors will lead to axial thermal deformation of the screw, with the deformation being the primary factor restricting the high precision micro-feed. Elman neural networks (ENs) are employed to carry out the thermal error modelling in this paper. To improve the performance of ENs, the differential evolution (DE) algorithm is used to optimize the initial weights and thresholds of the ENs. Complex operating conditions of the TDMS are also considered in the model. The experimental results show that the thermal error residual decreased from 1.73 to 0.88 μm for the DE-ELMAN model. Moreover, the proposed method of thermal error modelling proved to be accurate and robust when used in the varying conditions.
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