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СПИСОК ИСПОЛЬЗОВАННЫХ ИСТОЧНИКОВ
1.Lei, W.T. and Hsu, Y.Y. (2003) Accuracy Enhancement of Five-Axis CNC Machines through Real-Time Error Compensation. International Journal of Machine Tools and Manufacture, 43, 871-877. https://doi.org/10.1016/S0890-6955(03)00089-0
2.Ibaraki, S. and Knapp, W. (2012) Indirect Measurement of Volumetric Accuracy for Three-Axis and Five-Axis Machine Tools: A Review. International Journal of Automation Technology, 6, 110-124. https://doi.org/10.20965/ijat.2012.p0110
3.Tian, W.J., Gao, W.G., Chang, W.F., et al. (2014) Error Modeling and Sensitivity Analysis of a Five-Axis Machine Tool. Mathematical Problems in Engineering, 2014, Article ID: 745250. https://doi.org/10.1155/2014/745250
4.Chen, J.S. (1996) A Study of Thermally Induced Machine Tool Errors in Real Cutting Conditions. International Journal of Machine Tools and Manufacture, 36, 1401-1411. https://doi.org/10.1016/0890-6955(95)00096-8
5. Lee, S.K., Yoo, J.H. and Yang, M.S. (2003) Effect of Thermal Deformation on Machine Tool Slide Guide Motion. Tribology International, 36, 41-47. https://doi.org/10.1016/S0301-679X(02)00128-7
6. Yang, H. and Ni, J. (2005) Dynamic Neural Network Modeling for Nonlinear, Nonstationary Machine Tool Thermally Induced Error. International Journal of Machine Tools and Manufacture, 45, 455-465. https://doi.org/10.1016/j.ijmachtools.2004.09.004
7. Choi, J.P., Min, B.K. and Lee, S.J. (2004) Reduction of Machining Errors of a Three-Axis Machine Tool by On-Machine Measurement and Error Compensation System. Journal of Materials Processing Technology, 155, 2056-2064. https://doi.org/10.1016/j.jmatprotec.2004.04.402
8.Yang, J.G., Zhang, H.T., Tong, H.C., Cao, H.T. and Ren, Y.Q. (2005) Application of Real-Time Thermal Error Compensation for CNC Machine Tools. Journal of Shanghai Jiaotong University, 39, 1389-1392.
9. Shen, H., Fu, J., He, Y., et al. (2012) On-Line Asynchronous Compensation Methods for Static/Quasi-Static Error Implemented on CNC Machine Tools. International Journal of Machine Tools & Manufacture, 60, 14-26. https://doi.org/10.1016/j.ijmachtools.2012.04.003
10. Zhang, H., Zhou, Y.F., Tang, X.Q. and Chen, J.H. (2002) Error G Code Compensation Technology for CNC Machining Center. Journal of Huazhong University of Science and Technology, 30, 13-16.
11.Eskandari, S., Arezoo, B. and Abdullah, A. (2013) Positional, Geometrical, and Thermal Errors Compensation by Tool Path Modification Using Three Methods of Regression, Neural Networks, and Fuzzy Logic. The International Journal of Advanced Manufacturing Technology, 65, 1635-1649. https://doi.org/10.1007/s00170-012-4285-y
12.Zhu, S.W., Ding, G.F., Ma, S.W., et al. (2013) Workpiece Locating Error Prediction and Compensation in Fixtures. The International Journal of Advanced Manufacturing Technology, 67, 1423-1432. https://doi.org/10.1007/s00170-012-4578-1
13.Wu, C.J., Fan, J.W., Wang, Q.H., et al. (2018) Prediction and Compensation of Geometric Error for Translational Axes in Multi-Axis Machine Tools. The International Journal of Advanced Manufacturing Technology, 95, 3413-3435. https://doi.org/10.1007/s00170-017-1385-8
14.Xiang, S.T., Li, H.M., Deng, M., et al. (2018) Geometric Error Analysis and Compensation for Multi-Axis Spiral Bevel Gears Milling Machine. Mechanism and Machine Theory, 121, 59-74. https://doi.org/10.1016/j.mechmachtheory.2017.10.014
15. Lu, B.H., Ge, Y.J., Wang, Q.Y., et al. (2002) Prediction of Virtual NC Turning Accuracy. Journal of Mechanical Engineering, 38, 82-85.
16.Dang, J.W., Zhang, W.H., Wanmin, et al. (2011) New Prediction Model of Machining Error in Milling Process. Journal of Mechanical Engineering, 47, 150-155.
17. Ding, G.F., Zhu, S.W., Yahya, E., et al. (2014) Prediction of Machining Accuracy Based on a Geometric Error Model in Five-Axis Peripheral Milling Process. Journal of Engineering Manufacture, 228, 1226-1236.
18.Archenti, A. (2014) Prediction of Machined Part Accuracy from Machining System Capability. CIRP Annals—Manufacturing Technology, 63, 505-508. https://doi.org/10.1016/j.cirp.2014.03.040
