Correction of Errors in CNC Machining Centers with a Work Based Approach

Abstract

In this study, according to the zero defect concept that stands out in industry 4.0 applications, low cost compensation (compensation) method has been developed during the processing of the workpiece. Thanks to the new method, the axis errors that occurred during drilling were detected and the verification factor was found by using reference gauge blocks. By using this factor, numerical control (NC) codes have been updated at the rate of error. According to the results, it was concluded that accurate and traceable measurement can be made with the help of the measuring probe attached to the machine. It was concluded that this method can be used in precision parts manufacturing at a low cost. It has been determined that the axis error originating from the bench, whose hole distance is 55 μm for 500 mm, is realized in the tolerance range of ± 10 µm with the new method. With the help of compensation method, axis deviation values are improved by 81%.

Keywords

• Precise machining
• Machine errors
• Compensation
• Zero error

CNC İşleme Merkezlerinde Hataların İş Esaslı Yaklaşımla Düzeltilmesi

Abstract

Bu çalışmada, endüstri 4.0 uygulamalarında öne çıkan sıfır hata konseptine göre işparçasının işlenmesi sırasında düşük maliyetli kompanzasyon (telafi) metodu geliştirilmiştir. Yeni yöntem sayesinde, delik delme esnasında oluşan eksen hataları tespit edilmiş ve referans mastar blokları yardımıyla doğrulama faktörü bulunmuştur. Bu faktör kullanılarak, hata miktarı oranında nümerik kontrol (NC) kodları güncellenmiştir. Elde edilen sonuçlara göre, makine üzerine takılı ölçme probu yardımıyla doğru ve izlenebilir ölçüm yapılabildiği sonucuna ulaşılmıştır. Bu yöntemin düşük bir maliyet ile hassas parça imalatında kullanılabilir olduğu sonucuna varılmıştır. Delik mesafesi 500 mm için 55 μm olan tezgâhtan kaynaklanan eksen hatasının, yeni yöntemle ± 10 µm tolerans aralığında gerçekleştiği tespit edilmiştir. Kompanzasyon yöntemi yardımıyla eksen sapma değerleri %81 oranında iyileştirilmiştir.

Keywords

• Hassas üretim
• Tezgâh hataları
• Kompanzasyon
• Sıfır hata

References

1. [1] P. Katageri, B. S. Suresh, and A. Pasha Taj, "An approach to identify and select optimal temperature-sensitive measuring points for thermal error compensation modeling in CNC machines: A case study using cantilever beam," Materials Today: Proceedings, pp. 1-10, 2020.
2. [2] R. Ramesh, M. A. Mannan, and A. N. Poo, "Error compensation in machine tools - a review part I: geometric, cutting-force induced and fixture-dependent errors," Int J Mach Tool Manu, vol. 40, no. 9, pp. 1235-1256, 2000.
3. [3] A. C. Okafor and Y. M. Ertekin, "Derivation of machine tool error models and error compensation procedure for three axes vertical machining center using rigid body kinematics," Int J Mach Tool Manu, vol. 40, no. 8, pp. 1199-1213, 2000.
4. [4] D. Kono, A. Matsubara, I. Yamaji, and T. Fujita, "High-precision machining by measurement and compensation of motion error," Int J Mach Tool Manu, vol. 48, no. 10, pp. 1103-1110, 2008.
5. [5] C. Ma, L. Zhao, X. S. Mei, H. Shi, and J. Yang, "Thermal error compensation of high-speed spindle system based on a modified BP neural network," Int J Adv Manuf Tech, vol. 89, no. 9-12, pp. 3071-3085, 2017.
6. [6] K. F. Emann, B. T. Wu, and M. F. Devries, "A generalized geometric error model for multi-axis machines," CIRP Annals, vol. 36, no. 1, pp. 253-256, 1987.
7. [7] Jin-Hyeon Lee, Jae-Ha Lee, and S.-H. Yang, "Thermal error modeling of a horizontal machining center using fuzzy logic strategy," Journal of Manufacturing Processes, vol. 3, no. 2, pp. 120-127, 2001.
8. [8] P. Blaser, F. Pavlicek, K. Mori, J. Mayr, S. Weikert, and K. Wegener, "Adaptive learning control for thermal error compensation of 5-axis machine tools," J Manuf Syst, vol. 44, pp. 302-309, 2017.

9. [9] J. Mayr, M. Egeter, S. Weikert, and K. Wegener, "Thermal error compensation of rotary axes and main spindles using cooling power as input parameter," J Manuf Syst, vol. 37, pp. 542-549, 2015.
10. [10] T. N. Reddy, V. Shanmugaraj, P. Vinod, and S. G. Krishna, "Real-time thermal error compensation strategy for precision machine tools," Mater Today-Proc, vol. 22, pp. 2386-2396, 2020.
11. [11] Z. Huang, Y. C. Liu, L. Du, and H. Yang, "Thermal error analysis, modeling and compensation of five-axis machine tools," J Mech Sci Technol, vol. 34, no. 10, pp. 4295-4305, 2020.
12. [12] Martin Mareš, Otakar Horejš, and Lukáš Havlík, "Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated Python code validated with a machined test piece," Precision Engineering, vol. 66, pp. 21-30, 2020.
13. [13] H. T. Yue, C. G. Guo, Q. Li, L. J. Zhao, and G. B. Hao, "Thermal error modeling of CNC milling machining spindle based on an adaptive chaos particle swarm optimization algorithm," J Braz Soc Mech Sci, vol. 42, no. 8, 2020.
14. [14] C. Danjoua, J. L. Duigoua, and B. Eynarda, "Closed-loop manufacturing, a STEP-NC process for data feedback: a case study " in 48th CIRP Conference on Manufacturing Systems, Italy, 2016, pp. 852-857.
15. [15] T. Yandayan, R. Karadayı, ve İ. Teke, "İmalat işlemi sırasında ölçüm ve ölçüm verilerinin imalat için kullanımı," 8.Ulusal Ölçümbilim Kongresi, Kocaeli, 2013, pp. 1-10.
16. [16] G. L. P. Nijsse, "Linear motion systems; a modular approach for improved straightness performance," Ph.D. dissertation, Department of Machine Engineering, Delft University of Technology, Netherlands, 2001.
17. [17] A. Slocum, "Kinematic couplings: A review of design principles and applications," International Journal of Machine Tools and Manufacture, vol. 50, no. 4, pp. 310-327, 2010.
18. [18] Sina Eskandari, Behrooz Arezoo, and A. Abdullah, "Positional, geometrical, and thermal errors compensation by tool path modification using three methods of regression, neural networks, and fuzzy logic," Int J Adv Manuf Tech,vol. 65, no. 9-12, pp. 1635-1649, 2012.
19. [19] Y.C. Liang, W.D. Li, P. Lou, and J. M. Hu, "Thermal error prediction for heavy-duty CNC machines enabled by long short-term memory networks and fog-cloud architecture," J Manuf Syst, pp. 1-8 2020.