Modification of a neural network utilizing hybrid filters for the compensation of thermal deformation in machine tools

Yuan, Kun; Chang, Chuan-Wei; Huang, Yan‐Hua; Hsu, Chuag-Liang; Nieh, I-Fu

doi:10.1016/j.ijmachtools.2006.03.007

Cited by 79 publications

(38 citation statements)

References 12 publications

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“…This group 4 also includes error models exploiting: artificial neural networks, linear and nonlinear regression, dynamic models, transfer function, adaptation models, and other [31,33,34,119,193,197,198,207,208,209]. [76] Among the models in group 4, the model developed by Kim at al.…”

Section: Modelling and Computing Thermal Errors In Spindles And Rotatmentioning

confidence: 99%

Thermal issues in machine tools

et al. 2012

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Section: Modelling and Computing Thermal Errors In Spindles And Rotatmentioning

confidence: 99%

Thermal issues in machine tools

et al. 2012

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“…The search continues for an effective, universal, simple and cheap method of compensating for the elongation of ball screw transmission systems [6][7][8][9][10][11][12][15][16][17][18][19].…”

Section: Literature Reviewmentioning

confidence: 99%

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

2017

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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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“…The models can accurately map the empirical relationship between discrete temperature data of several points and the thermal deformation of machine tools [10,11]. A number of research works have been done in the past.…”

Section: Introductionmentioning

confidence: 99%

A Fourier Series-Neural Network Based Real-Time Compensation Approach for Geometric and Thermal Errors of CNC Milling Machines

Wang

Zhang

Fan

et al. 2013

Advances in Mechanical Engineering

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In order to improve the accuracy and efficiency of the real-time error compensation, a Fourier Series-Neural Network (FS-NN) based compensation methodology is developed to reduce the thermally induced geometric errors of CNC milling machines under varying conditions. Firstly, an error model is presented based on the principle of Fourier series with fast calculation and higher precision, which is regarded as the error base of original geometric errors. Secondly, the relationships between the slopes related to different error curves and the temperatures of key thermal points are figured out by using neural networks when concerning thermal effects. Then, a combined error model is established which is suitable for reducing axis positioning errors at any thermal status. Finally, a real-time dynamic error compensation system is developed featuring automatic error modeling and multiaxis synchronous compensation. The experimental results prove that the proposed methodology has satisfactory modeling accuracy and robustness to frequently changing working conditions.

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Modification of a neural network utilizing hybrid filters for the compensation of thermal deformation in machine tools

Cited by 79 publications

References 12 publications

Thermal issues in machine tools

Thermal issues in machine tools

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

A Fourier Series-Neural Network Based Real-Time Compensation Approach for Geometric and Thermal Errors of CNC Milling Machines

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