Abstract
Frequency response functions (FRFs) are one of the most useful methods for representing machine tool dynamics under force excitation. FRFs are usually obtained empirically through output measurements, and force excitations are controlled by an external device such as hammers or shakers. This study offers an operational identification method that utilizes the calculation of force applied during the process as an input for FRF identification. Force excitation is provided through the face milling of a thin-walled workpiece, and acceleration measurements are taken during the process. The FRF is calculated at a designated position by sampling workpiece-cutting tool contacts as individual tap tests and substituting a force calculation as input. Force coefficients need to be known for the force calculation. An experimental force coefficient identification method is proposed. In that case, a similar thin-walled workpiece at a point with known FRF and acceleration measurements is utilized. Results are confirmed with FRFs obtained in the same location for both FRF identification and force coefficient identification approaches.
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References
Allemang RJ, Brown DL (2020) Experimental modal analysis methods. In: Allemang R, Avitabile P (eds) Handbook of experimental structural dynamics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-6503-8_36-1
Bąk PA, Jemielniak K (2016) Automatic experimental modal analysis of milling machine tool spindles. Proc Inst Mech Eng B J Eng Manuf 230(9):1673–1683. https://doi.org/10.1177/0954405415623485
Bediz B, Gozen B, Korkmaz E, Ozdoganlar O (2014) Dynamics of ultra-high-speed (UHS) spindles used for micromachining. Int J Mach Tools Manuf 87:27–38. https://doi.org/10.1016/j.ijmachtools.2014.07.007
Li B, Cai H, Mao X, Huang J, Luo B (2013) Estimation of CNC machine–tool dynamic parameters based on random cutting excitation through operational modal analysis. Int J Mach Tools Manuf 71:26–40, ISSN 0890-6955. https://doi.org/10.1016/j.ijmachtools.2013.04.001
Li B, Luo B, Mao X, Cai H, Peng F, Liu H (2013) A new approach to identifying the dynamic behavior of CNC machine tools with respect to different worktable feed speeds. Int J Mach Tools Manuf 72:73–84, ISSN 0890-6955. https://doi.org/10.1016/j.ijmachtools.2013.06.004
Li B, Li L, He H et al (2019) Research on modal analysis method of CNC machine tool based on operational impact excitation. Int J Adv Manuf Technol 103:1155–1174. https://doi.org/10.1007/s00170-019-03510-x
Özşahin O, Budak E, Özgüven HN (2011) Investigating dynamics of machine tool spindles under operational conditions. In: Advanced materials research (vol. 223, pp. 610–621). Trans Tech Publications, Ltd. https://doi.org/10.4028/www.scientific.net/amr.223.610
Wang D, Pan Y (2017) A method to identify the main mode of machine tool under operating conditions. AIP Conf Proc 1829:020039. https://doi.org/10.1063/1.4979771
Cai H, Luo B, Mao X, Gui L, Song B, Li B, Peng F (2015) A method for identification of machine-tool dynamics under machining. Procedia CIRP 31:502–507. https://doi.org/10.1016/j.procir.2015.03.027
Berthold J, Kolouch M, Wittstock V, Putz M (2016) Broadband excitation of machine tools by milling forces for performing operational modal analysis. MM Sci J 2016:1473–1481. https://doi.org/10.17973/MMSJ.2016_11_2016164
Berthold J, Kolouch M, Wittstock V, Putz M (2018) Identification of modal parameters of machine tools during cutting by operational modal analysis. Procedia CIRP 77:473–476. https://doi.org/10.1016/j.procir.2018.08.268
Iglesias A, Munoa J, Ramírez C, Ciurana J, Dombovari Z (2016) FRF estimation through sweep milling force excitation (SMFE). Procedia CIRP 46:504–507. https://doi.org/10.1016/j.procir.2016.04.019
Hui C, Mao X, Li B, Luo B (2014) Estimation of FRFs of machine tools in output-only modal analysis. Int J Adv Manuf Technol 77:117–130
Pawełko P, Powałka B, Berczyński S (2013) Estimation of milling force model coefficients with regularized inverse problem. Advances in Manufacturing. Sci Technol 37(2):5–21. https://doi.org/10.2478/amst-2013-0012
Aggarwal S, Nešić N, Xirouchakis P (2013) Cutting torque and tangential milling force coefficient identification from spindle motor current. Int J Adv Manuf Technol 65:81–95. https://doi.org/10.1007/s00170-012-4152-x
Zhou J, Mao X, Liu H, Li B, Peng Y (2018) Prediction of cutting force in milling process using vibration signals of machine tool. Int J Adv Manuf Technol 99:965–984. https://doi.org/10.1007/s00170-018-2464-1
Yamato S, Imabeppu Y, Irino N, Suzuki N, Kakinuma Y (2019) Enhancement of sensor-less milling force estimation by tuning of observer parameters from cutting test. Procedia Manuf 41:272–279. https://doi.org/10.1016/j.promfg.2019.07.056
Iglesias A, Taner Tunç L, Özsahin O, Franco O, Munoa J, Budak E (2022) Alternative experimental methods for machine tool dynamics identification: a review. Mech Syst Signal Process 170:108837. https://doi.org/10.1016/j.ymssp.2022.108837
Quintana G, Ciurana J, Teixidor D (2008) A new experimental methodology for identification of stability lobes diagram in milling operations. Int J Mach Tools Manuf 48(15):1637–1645, ISSN 0890-6955. https://doi.org/10.1016/j.ijmachtools.2008.07.006
Grossi N, Sallese L, Scippa A, Campatelli G (2017) Improved experimental-analytical approach to compute speed-varying tool-tip frf. Precis Eng 48:114–122. https://doi.org/10.1016/j.precisioneng.2016.11.011
Duan Z, Li C, Ding W et al (2021) Milling force model for aviation aluminum alloy: academic insight and perspective analysis. Chin J Mech Eng 34:18. https://doi.org/10.1186/s10033-021-00536-9
Liu Q, Li ZQ (2011) Simulation and optimization of CNC milling process-modeling, algorithms and applications. Aviation industry press, Beijing
Zaghbani I, Songmene V (2009) A force-temperature model including a constitutive law for dry high speed milling of aluminium alloys. J Mater Process Technol 209(5):2532–2544. https://doi.org/10.1016/j.jmatprotec.2008.05.050
Shirase K, Altintaş Y (1996) Cutting force and dimensional surface error generation in peripheral milling with variable pitch helical end mills. Int J Mach Tools Manuf 36(5):567–584
Funding
This work is partially funded by the Scientific and Technological Research Council of Turkey under grant number TUBİTAK 218M430 and METU BAP with grant number AGEP-302-2022-10998.
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All authors contributed to the development of method and experimentation. Author Barış Altun has worked on development of the method and preparation experiment procedure. Author Hakan Çalışkan has worked on the experimentation procedure, and advised during theoretical development of identification methods. Author Orkun Özşahin has provided expertise and equipment at data acquisition, and advised during theoretical development of identification methods. All authors have contributed into editing the manuscript. All authors read and approved the final manuscript.
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Altun, B., Çalışkan, H. & Özşahin, O. Position-dependent FRF identification without force measurement in milling process. Int J Adv Manuf Technol 128, 4981–4996 (2023). https://doi.org/10.1007/s00170-023-11925-w
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DOI: https://doi.org/10.1007/s00170-023-11925-w