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IJAT Vol.18 No.3 pp. 342-351
doi: 10.20965/ijat.2024.p0342
(2024)

Research Paper:

Practical Method for Identifying Model Parameters for Machining Error Simulation in End Milling Through Sensor-Less Monitoring and On-Machine Measurement

Kazuki Kaneko, Arisa Kudo, Takanori Waizumi, Jun Shimizu, Libo Zhou, Hirotaka Ojima, and Teppei Onuki

Ibaraki University
4-12-1 Nakanarusawa-cho, Hitachi, Ibaraki 316-8511, Japan

Corresponding author

Received:
September 28, 2023
Accepted:
February 28, 2024
Published:
May 5, 2024
Keywords:
end milling, machining error simulation, parameter identification, tool stiffness, on-machine measurement
Abstract

Depending on cutting conditions, unacceptable machining errors are caused by tool deflection in end milling operations. Many studies have proposed methods for predicting the machining error owing to the tool deflection to achieve the theoretical optimization of the cutting conditions. However, the conventional machining error simulation is not practically utilized to determine the optimal cutting conditions. Tool system stiffness parameters and cutting coefficients must be identified in advance to simulate machining errors. However, dynamometers and displacement sensors are required for parameter identification. Therefore, it is impossible to identify the required parameters in typical factories, which do not possess such special equipment. In this study, a practical method was developed to identify the stiffness parameters that can be determined in factories. The proposed method employs on-machine measurement and sensor-less cutting force monitoring to achieve practical parameter identification. In the proposed method, the profile milling is first conducted. During the milling operation, the cutting force and cutting torque are monitored through a controller based on the sensor-less monitoring technique. After the operation, the machining error distribution on the machined surface is measured on machine using a touch probe. The required parameters are identified by minimizing the differences between the measured and theoretical forces, torques, and machining error distributions.

Cite this article as:
K. Kaneko, A. Kudo, T. Waizumi, J. Shimizu, L. Zhou, H. Ojima, and T. Onuki, “Practical Method for Identifying Model Parameters for Machining Error Simulation in End Milling Through Sensor-Less Monitoring and On-Machine Measurement,” Int. J. Automation Technol., Vol.18 No.3, pp. 342-351, 2024.
Data files:
References
  1. [1] W. Arai, F. Tanaka, and M. Onosato, “Error Estimation of Machined Surfaces in Multi-Axis Machining with Machine Tool Errors Including Tool Self-Intersecting Motion Based on High-Accuracy Tool Swept Volumes,” Int. J. Automation Technol., Vol.12, No.5, pp. 680-687, 2018. https://doi.org/10.20965/ijat.2018.p0680
  2. [2] S. Ibaraki and I. Yoshida, “A Five-Axis Machining Error Simulator for Rotary-Axis Geometric Errors Using Commercial Machining Simulation Software,” Int. J. Automation Technol., Vol.11, No.2, pp. 179-187, 2017. https://doi.org/10.20965/ijat.2017.p0179
  3. [3] R. Sato and K. Shirase, “Geometric Error Compensation of Five-Axis Machining Centers Based on On-Machine Workpiece Measurement,” Int. J. Automation Technol., Vol.12, No.2, pp. 230-237, 2018. https://doi.org/10.20965/ijat.2018.p0230
  4. [4] S. Zhu, G. Ding, S. Qin, J. Lei, L. Zhuang, and K. Yan, “Integrated Geometric Error Modeling, Identification and Compensation of CNC Machine Tools,” Int. J. Mach. Tools Manuf., Vol.52, Issue 1, pp. 24-29, 2012. https://doi.org/10.1016/j.ijmachtools.2011.08.011
  5. [5] J. Mayr, J. Jedrzejewski, E. Uhlmann, M. Alkan Donmez, W. Knapp, F. Härtig, K. Wendt, T. Moriwaki, P. Shore, R. Schmitt, C. Brecher, T. Würz, and K. Wegener, “Thermal Issues in Machine Tools,” CIRP Ann. – Manuf. Technol., Vol.61, No.2, pp. 771-791, 2012. https://doi.org/10.1016/j.cirp.2012.05.008
  6. [6] A. M. Abdulshahed, A. P. Longstaff, and S. Fletcher, “The Application of Anfis Prediction Models for Thermal Error Compensation on CNC Machine Tools,” Appl. Soft Comput., Vol.27, pp. 158-168, 2015. https://doi.org/10.1016/j.asoc.2014.11.012
  7. [7] J. Liu, C. Ma, H. Gui, and S. Wang, “Transfer Learning-Based Thermal Error Prediction and Control with Deep Residual LSTM Network,” Knowl.-Based Syst., Vol.237, Article No.107704, 2022. https://doi.org/10.1016/j.knosys.2021.107704
  8. [8] T. Huang, X.-M. Zhang, and H. Ding, “Tool Orientation Optimization for Reduction of Vibration and Deformation in Ball-End Milling of Thin-Walled Impeller Blades,” Procedia CIRP, Vol.58, pp. 210-215, 2017. https://doi.org/10.1016/j.procir.2017.03.211
  9. [9] K. Kaneko, J. Shimizu, and K. Shirase, “A Voxel-Based End Milling Simulation Method to Analyze the Elastic Deformation of a Workpiece,” J. of Manufacturing Science and Engineering, Vol.145, No.1, Article No.MANU-22-1321, 2023. https://doi.org/10.1115/1.4055794
  10. [10] K. Ichikawa, H. Saito, J. Kaneko, Y. Okuma, and K. Horio, “Estimation Method of Machining Error on Low Rigidity Workpiece for Tool Posture Planning,” Int. J. Automation Technol., Vol.11, No.6, pp. 964-970, 2017. https://doi.org/10.20965/ijat.2017.p0964
  11. [11] W. Li, L. Wang, and G. Yu, “Force-Induced Deformation Prediction and Flexible Error Compensation Strategy in Flank Milling of Thin-Walled Parts,” J. Mater Process Technol., Vol.297, Article No.117258, 2021. https://doi.org/10.1016/j.jmatprotec.2021.117258
  12. [12] H. Narita, “A Method for Using a Virtual Machining Simulation to Consider Both Equivalent CO2 Emissions and Machining Costs in Determining Cutting Conditions,” Int. J. Automation Technol., Vol.9, No.2, pp. 115-121, 2015. https://doi.org/10.20965/ijat.2015.p0115
  13. [13] X. Duan, F. Peng, K. Zhu, and G. Jiang, “Tool Orientation Optimization Considering Cutter, Deflection Error Caused by Cutting Force for Multi-Axis Sculptured Surface Milling,” Int. J. Adv. Manuf. Technol., Vol.103, pp. 1925-1934, 2019. https://doi.org/10.1007/s00170-019-03663-9
  14. [14] M. Soori, B. Arezoo, and M. Habibi, “Virtual Machining Considering Dimensional, Geometrical and Tool Deflection Errors in Three-Axis CNC Milling Machines,” J. Manuf. Syst., Vol.33, Issue 4, pp. 498-507, 2014. https://doi.org/10.1016/j.jmsy.2014.04.007
  15. [15] M. Habibi, B. Arezoo, and M. Nojedeh, “Tool Deflection and Geometrical Error Compensation by Tool Path Modification,” Int. J. Mach. Tools Manuf., Vol.51, Issue 6, pp. 439-449, 2011. https://doi.org/10.1016/j.ijmachtools.2011.01.009
  16. [16] N. Zeroudi and M. Fontaine, “Prediction of Tool Deflection and Tool Path Compensation in Ball-End Milling,” J. of Intelligent Manufacturing, Vol.26, pp. 425-445, 2015. https://doi.org/10.1007/s10845-013-0800-8
  17. [17] K. Kaneko, M. Inui, and I. Nishida, “Fast Simulation of Machining Error Induced by Elastic Deformation of Tool System in End Milling,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.17, Issue 3, Article No.JAMDSM0035, 2023. https://doi.org/10.1299/jamdsm.2023jamdsm0035
  18. [18] T. Kamigochi and Y. Kakinuma, “Development of an Intelligent Stage with Sensor-Less Cutting Force and Torque Monitoring Function,” Int. J. Automation Technol., Vol.6, No.6, pp. 736-741, 2012. https://doi.org/10.20965/ijat.2012.p0736
  19. [19] Y. Altintas and D. Aslan, “Integration of Virtual and On-Line Machining Process Control and Monitoring,” CIRP Ann., Vol.66, Issue 1, pp. 349-352, 2017. https://doi.org/10.1016/j.cirp.2017.04.047
  20. [20] J. Tlusty and P. MacNeil, “Dynamics of Cutting Forces in End Milling,” CIRP Ann. – Manuf. Technol., Vol.24, No.1, pp. 21-25, 1975.
  21. [21] R. Sato, M. Hasegawa, and K. Shirase, “Cutting Force Monitoring Based on the Frequency Analysis of Feed Motor Torques,” J. SME Japan, Vol.2, pp. 7-12, 2013.
  22. [22] K. Kaneko, I. Nishida, R. Sato, and K. Shirase, “A Practical Method to Monitor Tool Wear in End Milling Using a Changing Cutting Force Model That Requires No Additional Sensors,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.15, Issue 6, Article No.JAMDSM0077, 2021. https://doi.org/10.1299/jamdsm.2021jamdsm0077

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Last updated on May. 19, 2024