New approach to spindle thermal extension measuring based on machine vision for the vertical maching centre

• Autorzy: Su Dongxu , Cai Xin , Li Yang , Zhao Wanhuan , Zhang Huijie

Identyfikatory

DOI

10.24425/mms.2021.136612

• Języki publikacji: EN

Abstrakty

EN

When machine tool spindles are running at a high rotation speed, thermal deformation will be introduced due to the generation of large amounts of heat, and machining accuracy will be influenced as a result, which is a generalized issue in numerous industries. In this paper, a new approach based on machine vision is presented for measurements of spindle thermal error. The measuring system is composed of a Complementary Metal-Oxide-Semiconductor (CMOS) camera, a backlight source and a PC. Images are captured at different rotation angles during end milling process. Meanwhile, the Canny edge detection and Gaussian sub-pixel fitting methods are applied to obtain the bottom edge of the end mill which is then used to calculate the lowest point coordinate of the tool. Finally, thermal extension of the spindle is obtained according to the change of the lowest point at different time steps of the machining process. This method is validated through comparison with experimental results from capacitive displacement sensors. Moreover, spindle thermal extension during the processing can be precisely measured and used for compensation in order to improve machining accuracy through the proposed method.

Słowa kluczowe

EN

Spindle thermal extension measuring; machine vision; Gaussian sub-pixel fitting; thermal error compensation

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2021
• Tom: Vol. 28, nr 2
• Strony: 357--370
• Opis fizyczny: Bibliogr. 23 poz., rys., tab., wykr., wzory

Twórcy

autor

Su Dongxu

• Xi’an Jiaotong University, State Key Laboratory for Manufacturing System Engineering, Xi’an, Shaanxi 710054, China

autor

Cai Xin

• Xi’an Jiaotong University, State Key Laboratory for Manufacturing System Engineering, Xi’an, Shaanxi 710054, China

autor

Li Yang

• Xi’an Jiaotong University, State Key Laboratory for Manufacturing System Engineering, Xi’an, Shaanxi 710054, China

autor

Zhao Wanhuan

• Xi’an Jiaotong University, State Key Laboratory for Manufacturing System Engineering, Xi’an, Shaanxi 710054, China

autor

Zhang Huijie

• Xi’an Jiaotong University, State Key Laboratory for Manufacturing System Engineering, Xi’an, Shaanxi 710054, China

Bibliografia

• [1] Khan, A. (2020). Experimental Study of the Heat Transfer Enhancement in Concentric Tubes With Spherical and Pyramidal Protrusions. Journal of Applied and Computational Mechanics, 6(4), 801-812. https://doi.org/10.22055/jacm.2019.30122.1686
• [2] Sarhan, A. A. D. (2014). Investigate the spindle errors motions from thermal change for high-precision CNC machining capability. The International Journal of Advanced Manufacturing Technology, 70(5-8), 957-963. https://doi.org/10.1007/s00170-013-5339-5
• [3] Li, Y., Zhao, W., Lan, S., Ni, J., Wu, W., & Lu, B. (2015). A review on spindle thermal error compensation in machine tools. International Journal of Machine Tools and Manufacture, 95, 20-38. https://doi.org/10.1016/j.ijmachtools.2015.04.008
• [4] Liu, T., Gao, W., Zhang, D., Zhang, Y., Chang, W., Liang, C., & Tian, Y. (2017). Analytical Modeling for Thermal Errors of Motorized Spindle Unit. International Journal of Machine Tools & Manufacture, 112, 53-70. https://doi.org/10.1016/j.ijmachtools.2016.09.008
• [5] Moriwaki, T., & Shamoto, E. (1998). Analysis of Thermal Deformation of an Ultraprecision Air Spindle System. CIRP Annals-Manufacturing Technology, 47(1), 315-319. https://doi.org/10.1016/S0007-8506(07)62841-8
• [6] Li, Y., Zhao, W., Wu, W., & Lu, B. (2017). Boundary conditions optimization of spindle thermal error analysis and thermal key points selection based on inverse heat conduction. The International Journal of Advanced Manufacturing Technology, 90(9-12), 2803-2812. https://doi.org/10.1007/s00170-016-9594-0
• [7] Li, T., Li, F., Jiang, Y., Zhang, J., & Wang, H. (2017). Kinematic calibration of a 3-P(Pa)S parallel-type spindle head considering the thermal error. Mechatronics, 43, 86-98. https://doi.org/j.mechatronics.2017.03.002
• [8] Srinivasa, N., Ziegert, J. C., & Mize, C. D. (1996). Spindle thermal drift measurement using the laser ball bar. Precision Engineering, 18(2), 118-128. https://doi.org/10.1016/0141-6359(95)00053-4
• [9] Ibaraki, S., Inui, H., Hong, C., Nishikawa, S., & Shimoike, M. (2019). On-machine identification of rotary axis location errors under thermal influence by spindle rotation. Precision Engineering, 55, 42-47. https://doi.org/10.1016/j.precisioneng.2018.08.005
• [10] Jurkovic, J., Korosec, M., & Kopac, J. (2005). New approach in tool wear measuring technique using CCD vision system. International Journal of Machine Tools and Manufacture, 45(9), 1023-1030. https://doi.org/10.1016/j.ijmachtools.2004.11.030
• [11] Dai, Y., & Zhu, K. (2017). A machine vision system for micro-milling tool condition monitoring. Precision Engineering, 52, S780782625. https://doi.org/10.1016/j.precisioneng.2017.12.006
• [12] Wang, S. M., Yu, H. J., Liu, S. H., & Chen, D. F. (2011). An on-machine and vision-based depth-error measurement method for micro machine tools. International Journal of Precision Engineering & Manufacturing, 12(6), 1071-1077. https://doi.org/10.1007/s12541-011-0143-3
• [13] Kavitha, C., & Ashok, S. D. (2017). A New Approach to Spindle Radial Error Evaluation Using a Machine Vision System. Metrology and Measurement Systems, 24(1), 201-219. https://doi.org/10.1515/mms-2017-0018
• [14] Overington, I., & Greenway, P. (1987). Practical first-difference edge detection with subpixel accuracy. Image & Vision Computing, 5(3), 217-224. https://doi.org/10.1016/0262-8856(87)90052-7
• [15] Tabbone, S., & Ziou, D. (1992). Subpixel positioning of edges for first and second order operators. 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis, Netherlands. https://doi.org/10.1109/ICPR.1992.202071
• [16] Lyvers, E. P., Mitchell, O. R., Akey, M. L., & Reeves, A. P. (1989). Subpixel Measurement Using a Moment-Based Edge Operator. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(12), 1293-1309. https://doi.org/10.1109/34.41367
• [17] Tabatabai, A. J., & Mitchell, O. R. (1984). Edge location to subpixel values in digital imagery. IEEE Transactions on Pattern Analysis & Machine Intelligence, PAMI-6(2), 188-201. https://doi.org/10.1109/TPAMI.1984.4767502
• [18] Yu, W., Ma, Y., Wu, X., & Liu, K. (2015). Research of improved subpixel edge detection algorithm using Zernike moments. Chinese Automation Congress (CAC), China, 712-716. https://doi.org/10.1109/CAC.2015.7382590
• [19] Nalwa, V. S., & Binford, T. O. (1986). On detecting edges. IEEE Transactions on Pattern Analysis & Machine Intelligence, PAMI-8(6), 699-714. https://doi.org/10.1109/TPAMI.1986.4767852
• [20] Su, C. Y., Yu, L. A., & Chen, N. K. (2016). Effective subpixel edge detection for LED probes. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Hungary, 000379-000382. https://doi.org/10.1109/SMC.2016.7844270
• [21] Ye, J., Fu, G., & Poudel, U. P. (2005). High-accuracy edge detection with blurred edge model. Image and Vision Computing, 23(5), 453-467. https://doi.org/10.1016/j.imavis.2004.07.007
• [22] Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis & Machine Intelligence, PAMI-8(6), 679-698. https://doi.org/10.1016/B978-0-08-051581-6.50024-6
• [23] Ding, L., & Goshtasby, A. (2001). On the Canny edge detector. Pattern Recognition, 34(3), 721-725. https://doi.org/10.1016/S0031-3203(00)00023-6

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).