期刊
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
卷 45, 期 4-5, 页码 455-465出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijmachtools.2004.09.004
关键词
machine tool; thermal error; error compensation; dynamic neural network
This paper presents a new modeling methodology for nonstationary machine tool thermal errors. The method uses the dynamic neural network model to track nonlinear time-varying machine tool errors under various thermal conditions. To accommodate the nonstationary nature of the thermo-elastic process, an Integrated Recurrent Neural Network (IRNN) is introduced to identify the nonstationarity of the thermo-elastic process with a deterministic linear trend. Experiments on spindle thermal deformation are conducted to evaluate the model performance in terms of model estimation accuracy and robustness. The comparison indicates that the IRNN performs better than other modeling methods, such as, multi-variable regression analysis (MRA), multi-layer feedforward neural network (MFN), and recurrent neural network (RNN). in terrns of model robustness under a variety of working conditions. (C) 2004 Elsevier Ltd. All rights reserved.
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