期刊
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
卷 17, 期 9, 页码 665-669出版社
SPRINGER LONDON LTD
DOI: 10.1007/PL00003948
关键词
cutting force; neural network; turning; workpiece error
A neural network method is presented for redicting cutting-force-induced errors in real-time during tuning operations based on the estimated cutting forces. Workpiece errors can be considerably affected by the deflections of the machine-workpiece-tool system. A model of the elastic deflections of the machine-workpiece-tool system due to the cutting force in turning developed. A novel radial basis function (RBF) neural network is used to map the relationship between the cutting-force components (radial, axial and tangential) and the consequent dimensional deviation of the finished parts caused by the combined deflections of the machine-workpiece-tool system. Cutting tests were performed on a two-axis CNC turning centre and the experimental results showed that the prediction accuracy of the maximum diameter error of the workpiece was within 15%. The trained RBF neural network was able to predict the cutting force induced error in real-time during turning.
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