Original scientific paper

https://doi.org/10.17559/TV-20161207171012

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

Izabela Rojek ; Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
Michał Kowal ; Institute of Mechanical Technology, Poznan University of Technology, Piotrowo 3, 60-965 Poznań, Poland
Antun Stoic ; Mechanical Engineering Faculty of Slavonski Brod, J. J. Strossmayer University of Osijek, Trg Ivane Brlic Mazuranic 2, 35000 Slavonski Brod, Croatia

Abstract

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

Hrčak ID:

190165

URI

https://hrcak.srce.hr/190165

Publication date:

3.12.2017.

Article data in other languages: croatian

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