Tehnički vjesnik, Vol. 24 No. 6, 2017.
Izvorni znanstveni članak
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
Sažetak
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.
Ključne riječi
ball screw; CNC machine tool; neural network; prediction model; thermal compensation
Hrčak ID:
190165
URI
Datum izdavanja:
3.12.2017.
Posjeta: 3.006 *