Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 59, Issue 9-12, Pages 1065-1072Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00170-011-3564-3
Keywords
Machine tool; Thermal error; Error compensation; Grey model; Artificial neural network; Spindle deformation
Funding
- Chinese National Science and Technology Key Special Projects Top Grade CNC Machine Tools and Basic Manufacturing Equipment [2011ZX04015-031]
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This paper proposes a novel modeling methodology for machine tool thermal error. This method combines the advantages of both grey model and artificial neural network (ANN) in terms of data processing. To enhance the robustness and the prediction accuracy, two kinds of grey neural network, namely serial grey neural network (SGNN) and parallel grey neural network (PGNN), are proposed to predict the thermal error. Experiments on the axial directional spindle deformation on a five-axis machining center are conducted to build and validate the proposed models. The results show that both SGNN and PGNN perform better than the traditional grey model and ANN in terms of prediction accuracy and robustness. So the new models are more suitable for complex working conditions in industrial applications.
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