Browsing by Author "Turyagyenda, G."

Now showing 1 - 3 of 3
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    Modeling and Real-time Compensation of Cutting Force-induced Errors on NC Turning Center
    (Trans Tech Publications Ltd., 2006) Wu, H.; Turyagyenda, G.; Yang, J.G.
    This paper systematically presents the relationship between cutting forced-induced errors and the spindle motor current basing on kinematic chain of NC machine tools. Constructs the model of cutting force-induced errors with BP Neural network, and develops the real-time error compensation system. The compensation effect of this system is verified through the experiment and the compensation system is of a great importance to precision manufacturing industry.
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    Precise account of frictional loads in motor current-based compensation system of cutting force-induced error
    (Journal of Mechanical Engineering Science, 2007) Turyagyenda, G.; Yang, J.; Wu, H.
    The balance between precision and its cost implications is a major determining factor for the competitiveness in the contemporary machining industry. The current paper presents an inexpensive compensation system for cutting force induced error based on current sensory system for indirect monitoring of cutting force. Based on machining parameters and online sensor signal, a dedicated model predicts the corresponding error. The model precisely accounts for the complex phenomena of coulomb and viscous frictions in the transmission system. Finally, the predicted error is compensated by means of external shifting of respective coordinate system origins in real-time. This system is convenient and reliable and, in addition, precise and easy to repair. The experimental evaluation of the system demonstrated robustness and great enhancement of machine accuracy.
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    Study on the Application of the Combined Prediction Modeling Method to Thermal Error Modeling on NC Machine Tools
    (Trans Tech Publications Ltd, 2007) Li, Y.X.; Yang, J.G.; Zhang, H.T.; Turyagyenda, G.
    Due to the complexity of machine tool thermal errors affected by various factors, a new combining prediction model, based on the theory of gery system GM (1,1) model, is applied to the trend prediction of machine tool thermal errors. The degree of smoothness of primary data sequence is first improved by function transform method and sequentially grey system GM (1,1) model is established; second, time series analysis model is established by remnant sequence of GM (1,1) model to amend the precision of grey system GM (1,1) model. Thus, the precision of combining prediction model is further improved. Through the prediction study on thermal error modeling in a spot NC turning center, testing results showed that combining prediction model can highly improve machine tool’s prediction precision and make it more effective for real-time compensation of machine tool thermal error.