Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

Abstract

Original scientific paper The need to improve the accuracy of positioning of a servo-drive was the stimulus for research on a new sensorless method for compensation of thermal deformations of ball screws, enabling predictive compensation of the elongation of such a screw based on historical data. Models have been developed for the predictive compensation of thermal deformations of ball screws in CNC machines, in the form of single-directional multi-layered neural networks with error back-propagation (MLP), radial basis function neural networks (RBF) and Kohonen networks. Neural networks were developed with different structures and learning parameters, and these networks were compared. Models were evaluated in terms of the effectiveness of operation of the networks. The models were tested on real data. Keywords: ball screw; CNC machine tool; neural network; prediction model; thermal compensation Predvidiva kompenzacija toplinskih deformiranja kuglastih vijaka u CNC strojevima primjenom neuronskih mrežaIzvorni znanstveni članak Potreba za povećanjem točnosti pozicioniranja servo-pogona bila je poticaj za istraživanje nove metode bez senzora za kompenzaciju toplinskih deformiranja kuglastih vijaka, koja će omogućiti predvidivu kompenzaciju izduženja takvih vijaka na temelju prikupljenih podataka. Razvijeni su modeli za predvidivu kompenzaciju toplinskih deformiranja kuglastih vijaka u CNC strojevima, u obliku jednosmjernih višeslojnih neuronskih mreža s unatražnim rasprostiranjem greške (MLP), neuronskih mreža s funkcijom radijalne baze (RBF) i Kohonen mreža. Razvijene su neuronske mreže s različitim strukturama i parametrima učenja, i te su se mreže uspoređivale. Modeli su se procjenjivali prema učinkovitosti mreža. Modeli su se ispitivali s realnim podacima.

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