ENHANCING CNC PRECISION: A REVIEW OF GEOMETRIC ERRORS AND SIMULATION METHODS IN THREE-AXIS CNC SYSTEMS

Supplementary Files

PDF

Keywords

Three-axis CNC
Geometric error
Finite element analysis
Numerical coding
Simulation

Abstract

The rapid expansion in industrial production has markedly increased the deployment of computer numerical control (CNC) systems. Recent efforts have concentrated on optimizing these machines for time-efficient cutting tool operations. The accuracy of CNC systems is heavily dependent on their geometric configurations, but geometric errors such as alignment deviations, backlash, and thermal deformation can compromise structural integrity and operational precision. This review focuses on the role of simulation in three-axis CNC machining, particularly through techniques like finite element analysis (FEA), to predict and mitigate these errors. By examining various CNC machine components, the review highlights how simulation methodologies can address geometric inaccuracies. Key findings indicate that integrating advanced simulation tools with CNC systems effectively reduces geometric errors, enhances machining accuracy, and improves overall system performance. This integration leads to more reliable and precise machining operations, thereby advancing the efficiency and effectiveness of CNC systems in high-precision manufacturing environments.

https://doi.org/10.35934/segi.v9i1.108

References

Chryssolouris, G., Mavrikios, D., Papakostas, N., Mourtzis, D., Michalos, G., & Georgoulias, K. (2009). Digital manufacturing: history, perspectives, and outlook. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(5), 451-462. doi: 10.1243/09544054JEM1241.

Cohen, Y., Faccio, M., Pilati, F., & Yao, X. (2019). Design and management of digital manufacturing and assembly systems in the Industry 4.0 era. The International Journal of Advanced Manufacturing Technology, 105, 3565-3577. doi: 10.1007/s00170-019-04595-0.

Cui, G., Lu, Y., Li, J., Gao, D., & Yao, Y. (2012). Geometric error compensation software system for CNC machine tools based on NC program reconstructing. The International Journal of Advanced Manufacturing Technology, 63, 169-180. doi: 10.1007/s00170-011-3895-0.

doi: https://doi.org/10.1016/j.ijmachtools.2006.01.004.

Flynn, J. M., Muelaner, J. E., Dhokia, V., & Newman, S. T. (2016). Improving error models of machine tools with metrology data. Procedia CIRP, 52, 204-209. doi: https://doi.org/10.1016/j.procir.2016.07.053.

Hao, X., Li, Y., Cheng, Y., Liu, C., Xu, K., & Tang, K. (2020). A time-varying geometry modeling method for parts with deformation during machining process. Journal of manufacturing systems, 55, 15-29. doi:https://doi.org/10.1016/j.jmsy.2020.02.002.

Huang, W., & Kong, Z. (2008). Simulation and integration of geometric and rigid body kinematics errors for assembly variation analysis. Journal of manufacturing systems, 27(1), 36-44.

Ibaraki, S., & Hata, T. (2010). A new formulation of laser step diagonal measurement—Three-dimensional case. Precision Engineering, 34(3), 516-525.

Ibaraki, S., & Knapp, W. (2012). Indirect measurement of volumetric accuracy for three-axis and five-axis machine tools: a review. International Journal of Automation Technology, 6(2), 110-124.

Ibaraki, S., Takeuchi, K., Yano, T., Takatsuji, T., Osawa, S., & Sato, O. (2012). Estimation of three-dimensional volumetric errors of numerically controlled machine tools by a tracking interferometer. Journal of Mechanics Engineering and Automation, 1(4), 313-319.

Jozwik, J., Kuric, I., & Semotiuk, L. (2014). Laser interferometer diagnostics of CNC machine tools. Communications-Scientific Letters of the University of Zilina, 16(3A), 169-175.

Józwik, J., Kuric, I., Sága, M., & Lonkwic, P. (2014). Diagnostics of CNC machine tools in manufacturing process with laser interferometer technology. Manufacturing technology, 14(1), 23-30. [Online]. Available: https://doi.org/10.xxxx/mft.2014.005.

Józwik, J., Mazurek, P., Wieczorek, M., & Czwarnowski, M. (2015). Linear positioning errors of 3-axis machine tool. Applied Computer Science, 11(2).

Kadir, A. A., Xu, X., & Hämmerle, E. (2011). Virtual machine tools and virtual machining—a technological review. Robotics and computer-integrated manufacturing, 27(3), 494-508. doi: https://doi.org/10.1016/j.rcim.2010.10.003.

Kiridena, V. F. P. M., & Ferreira, P. M. (1993). Mapping the effects of positioning errors on the volumetric accuracy of five-axis CNC machine tools. International Journal of Machine Tools and Manufacture, 33(3), 417-437.doi: https://doi.org/10.1016/0890-6955(93)90049-Z.

Konka, P., Lingam, R., Singh, U. A., Shivaprasad, C. H., & Reddy, N. V. (2020). Enhancement of accuracy in double sided incremental forming by compensating tool path for machine tool errors. The International Journal of Advanced Manufacturing Technology, 111, 1187-1199. doi: 10.1007/s00170-020-06149-1.

Lan, T. S. (2010). Tool wear optimization for general CNC turning using fuzzy deduction. Engineering, 2(12), 1019. doi: 10.4236/eng.2010.212128.

Lavernhe, S., Quinsat, Y., Tournier, C., Lartigue, C., & Mayer, R. (2008, June). NC-simulation for the prediction of surface finish in 5-axis High-Speed Machining. In 3rd CIRP International Conference on High Performance Cutting, Dublin (Ireland), (Vol. 1, pp. 387-396).

Lee, R. S., & Lin, Y. H. (2012). Applying bidirectional kinematics to assembly error analysis for five-axis machine tools with general orthogonal configuration. The International Journal of Advanced Manufacturing Technology, 62, 1261-1272. doi: 10.1007/s00170-011-3860-y.

Lin, W., & Fu, J. (2006, November). Modeling and application of virtual machine tool. In 16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06) (pp. 16-19). IEEE. doi: 10.1109/ICAT.2006.85.

Merdol, S. D., & Altintas, Y. (2008). Virtual cutting and optimization of three-axis milling processes. International Journal of Machine Tools and Manufacture, 48(10), 1063-1071. doi: https://doi.org/10.1016/j.ijmachtools.2008.03.004.

Möhring, H. C., Wiederkehr, P., Erkorkmaz, K., & Kakinuma, Y. (2020). Self-optimizing machining systems. CIRP Annals, 69(2), 740-763.doi: https://doi.org/10.1016/j.cirp.2020.05.007.

Mourtzis, D., Papakostas, N., Mavrikios, D., Makris, S., & Alexopoulos, K. (2015). The role of simulation in digital manufacturing: applications and outlook. International journal of computer integrated manufacturing, 28(1), 3-24. doi: 10.1080/0951192X.2013.800234.

Muelaner, J. E., Yang, B. R., Davy, C., Verma, M. R., & Maropoulos, P. G. (2014). Rapid machine tool verification. Procedia CIRP, 25, 431-438. doi: https://doi.org/10.1016/j.procir.2014.10.060.

Pawe?, M., & Bartosz, P. (2019). Rapid method to determine accuracy and repeatability of positioning of numerically controlled axes. International Journal of Machine Tools and Manufacture, 137, 1-12. doi: https://doi.org/10.1016/j.ijmachtools.2018.09.006.

Quinsat, Y., Lavernhe, S., & Lartigue, C. (2011). Characterization of 3D surface topography in 5-axis milling. Wear, 271(3-4), 590-595. doi: 10.1016/j.wear.2010.05.014.

Rao, V. S., & Rao, P. V. M. (2006). Tool deflection compensation in peripheral milling of curved geometries. International Journal of Machine Tools and Manufacture, 46(15), 2036-2043.

Sawula, D. A., Lin, Y. P., Fleisig, R. V., & Spence, A. D. (2012). Flexible Tool-path Generation for Variable Geometry. In Enabling Manufacturing Competitiveness and Economic Sustainability: Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual production (CARV2011), Montreal, Canada, 2-5 October 2011 (pp. 299-304). Springer Berlin Heidelberg. doi: 10.1007/978-3-642-23860-4_49.

Schwenke, H., Knapp, W., Haitjema, H., Weckenmann, A., Schmitt, R., & Delbressine, F. (2008). Geometric error measurement and compensation of machines—an update. CIRP annals, 57(2), 660-675. doi: https://doi.org/10.1016/j.cirp.2008.09.008.

Shimizu, Y., Jang, S., & Gao, W. (2016). Design and testing of an optical configuration for multi-dimensional measurement of a diamond cutting tool. Measurement, 94, 934-941.doi:https://doi.org/10.1016/j.measurement.2015.11.040.

Vo, A. T., Tran, N. H., Duong, T. H., & Kim, H. C. (2017). A new method for measuring generalized geometric errors for a new type of coordinate measuring machine using a laser tracker. Experimental Techniques, 41, 463-473.doi: 10.1007/s40799-017-0186-1.

Wang, L., Ding, H., Feng, J., Wang, S., Xiao, A., & Koch, D. (2010, December). Implementation of integrated manufacturing of free-form surfaces. In 2010 International Conference on Digital Manufacturing & Automation (Vol. 1, pp. 830-833). IEEE. doi: 10.1109/ICDMA.2010.228.

Wang, Y., Yin, C., Li, L., Zha, W., Pu, X., Wang, Y., ... & He, Y. (2020). Modeling and optimization of dynamic performances of large-scale lead screws whirl milling with multi-point variable constraints. Journal of Materials Processing Technology, 276, 116392.doi: https://doi.org/10.1016/j.jmatprotec.2019.116392.

Wu, Y. H., Gao, Q., & Zhao, D. H. (2012). Virtual Machining Technology Based on UG and VERICUT. Advanced Materials Research, 452, 1267-1271.doi: 10.4028/www.scientific.net/AMR.452-453.1267.

Xiang, S., Deng, M., Li, H., Du, Z., & Yang, J. (2019). Volumetric error compensation model for five-axis machine tools considering effects of rotation tool center point. The International Journal of Advanced Manufacturing Technology, 102, 4371-4382.doi: 10.1007/s00170-019-03497-5.

Xu, J., Wang, Y., Zhang, X., & Chang, S. (2013). Contour-parallel tool path generation for three-axis mesh surface machining based on one-step inverse forming. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227(12), 1800-1807. doi: 10.1177/0954405413492965.

Yao, Y., Zhao, H., Li, J., & Yuan, Z. (2006). Modeling of virtual workpiece with machining errors representation in turning. Journal of Materials Processing Technology, 172(3), 437-444.doi: https://doi.org/10.1016/j.jmatprotec.2005.11.005.

Z?bala, W., & Plaza, M. (2014). Comparative study of 3-and 5-axis CNC centers for free-form machining of difficult-to-cut material. International Journal of Production Economics, 158, 345-358. doi: https://doi.org/10.1016/j.ijpe.2014.08.006.

Zhu, S., Ding, G., Qin, S., Lei, J., Zhuang, L., & Yan, K. (2012). Integrated geometric error modeling, identification and compensation of CNC machine tools. International journal of machine tools and manufacture, 52(1), 24-29. doi: 10.1016/j.ijmachtools.2011.08.011.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2024 Array