Abstract
Forced vibration is a type of stable vibration different from chatter, which reduces the machining quality of a formed surface. The forced vibration characteristics of a thin-walled workpiece are difficult to capture due to varied milling excitation, cutter runout, and position dependence. Currently, insight into forced vibrations is still lacking without effective analysis methods. To solve these problems, a single-point online characterization method correlated to the mode shape was proposed. The modal contribution equation for a milled thin-walled workpiece was obtained by correlating the structural dynamics with the cutting force characteristics. Then, correlation factors were integrated to construct a state-space model, and the Kalman filter algorithm was used to estimate the modal contribution coefficients online based on a single-point response signal. Next, the principal mode shape was determined to characterize the forced vibration characteristics. The effectiveness of the proposed method was demonstrated by comparison with the operational deflection shape (ODS) results. These results revealed the contribution mechanism of each mode shape to the forced vibration under position-dependent excitation and varied cutting parameters, from which the vibration forms could be monitored.
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Data availability
The measuring data using the data acquisition instrument LMS SCADAS in our paper are available from the corresponding author by request, and other related materials can also be obtained from the corresponding author.
Code availability
The code for the Kalman filter algorithm used during the study is available from the corresponding author upon request.
References
Bolar G, Das A, Joshi SN (2018) Measurement and analysis of cutting force and product surface quality during end-milling of thin-wall components. Measurement 121:190–204. https://doi.org/10.1016/j.measurement.2018.02.015
Wang SQ, He CL, Li JG, Wang J (2021) Vibration-free surface finish in the milling of a thin-walled cavity part using a corn starch suspension. J Mater Process Technol 290:116980. https://doi.org/10.1016/j.jmatprotec.2020.116980
Zhuo Y, Han Z, An D, Jin H (2021) Surface topography prediction in peripheral milling of thin-walled parts considering cutting vibration and material removal effect. Int J Mech Sci 211:106797. https://doi.org/10.1016/j.ijmecsci.2021.106797
Yang Y, Zhang W-H, Ma Y-C, Wan M (2016) Chatter prediction for the peripheral milling of thin-walled workpieces with curved surfaces. Int J Mach Tools Manuf 109:36–48. https://doi.org/10.1016/j.ijmachtools.2016.07.002
Sun Y, Jiang S (2018) Predictive modeling of chatter stability considering force-induced deformation effect in milling thin-walled parts. Int J Mach Tools Manuf 135:38–52. https://doi.org/10.1016/j.ijmachtools.2018.08.003
Wang SY, Song QH, Liu ZQ (2019) Vibration suppression of thin-walled workpiece milling using a time-space varying PD control method via piezoelectric actuator. Int J Adv Manuf Technol 105(7–8):2843–2856. https://doi.org/10.1007/s00170-019-04493-5
Jung HJ, Hayasaka T, Shamoto E, Xu LJ (2020) Suppression of forced vibration due to chip segmentation in ultrasonic elliptical vibration cutting of titanium alloy Ti-6Al-4V. Precis Eng 64:98–107. https://doi.org/10.1016/j.precisioneng.2020.03.017
Huang CY, Junz Wang JJ (2010) A pole/zero cancellation approach to reducing forced vibration in end milling. Int J Mach Tools Manuf 50(7):601–610. https://doi.org/10.1016/j.ijmachtools.2010.03.011
Moradi H, Vossoughi G, Movahhedy MR, Ahmadian MT (2013) Forced vibration analysis of the milling process with structural nonlinearity, internal resonance, tool wear and process damping effects. Int J Non-linear Mech 54:22–34. https://doi.org/10.1016/j.ijnonlinmec.2013.02.005
Yao ZQ, Luo M, Mei JW, Zhang DH (2021) Position dependent vibration evaluation in milling of thin-walled part based on single-point monitoring. Measurement 171:108810. https://doi.org/10.1016/j.measurement.2020.108810
Zhao XJ, Ji C, Bi SF (2021) Spatial correlation effect of a multidimensional force on vibration suppression. Aerosp Sci Technol 117:106928. https://doi.org/10.1016/j.ast.2021.106928
Totis G, Insperger T, Sortino M, Stepan G (2019) Symmetry breaking in milling dynamics. Int J Mach Tools Manuf 139:37–59. https://doi.org/10.1016/j.ijmachtools.2019.01.002
Seguy S, Dessein G, Arnaud L (2008) Surface roughness variation of thin wall milling, related to modal interactions. Int J Mach Tools Manuf 48(3–4):261–274. https://doi.org/10.1016/j.ijmachtools.2007.09.005
Liu D, Luo M, Zhang Z, Hu Y, Zhang D (2022) Operational modal analysis based dynamic parameters identification in milling of thin-walled workpiece. Mech Syst Signal Process 167:108469. https://doi.org/10.1016/j.ymssp.2021.108469
Li ZL, Tuysuz O, Zhu LM, Altintas Y (2018) Surface form error prediction in five-axis flank milling of thin-walled parts. Int J Mach Tools Manuf 128:21–32. https://doi.org/10.1016/j.ijmachtools.2018.01.005
Ding Y, Zhu L (2016) Investigation on chatter stability of thin-walled parts considering its flexibility based on finite element analysis. Int J Adv Manuf Technol 94(9–12):3173–3187. https://doi.org/10.1007/s00170-016-9471-x
Li WT, Wang LP, Yu G, Wang D (2021) Time-varying dynamics updating method for chatter prediction in thin-walled part milling process. Mech Syst Signal Process 159:107840. https://doi.org/10.1016/j.ymssp.2021.107840
Kiss AK, Hajdu D, Bachrathy D, Stepan G (2018) Operational stability prediction in milling based on impact tests. Mech Syst Signal Process 103:327–339. https://doi.org/10.1016/j.ymssp.2017.10.019
Bachrathy D, Kiss AK, Kossa A, Berezvai S, Hajdu D, Stepan G (2020) In-process monitoring of changing dynamics of a thin-walled component during milling operation by ball shooter excitation. J Manuf Mater Proc 4(3):78. https://doi.org/10.3390/jmmp4030078
Kim S, Ahmadi K (2019) Estimation of vibration stability in turning using operational modal analysis. Mech Syst Signal Process 130:315–332. https://doi.org/10.1016/j.ymssp.2019.04.057
Jiang X, Jiang F (2020) Operational modal analysis using symbolic regression for a nonlinear vibration system. J Low Freq Noise V A 40(1):120–134. https://doi.org/10.1177/1461348420905172
Storti GC, Carrer L, da Silva Tuckmantel FW, Machado TH, Cavalca KL, Bachschmid N (2021) Simulating application of operational modal analysis to a test rig. Mech Syst Signal Process 153:107529. https://doi.org/10.1016/j.ymssp.2020.107529
Zhong J, Zhang J, Zhi X, Fan F (2018) Identification of dominant modes of single-layer reticulated shells under seismic excitations. Thin-Walled Structures 127:676–687. https://doi.org/10.1016/j.tws.2018.03.004
Zhou J, Li Z, Chen J (2018) Damage identification method based on continuous wavelet transform and mode shapes for composite laminates with cutouts. Compos Struct 191:12–23. https://doi.org/10.1016/j.compstruct.2018.02.028
HuemerKals S, Kappauf J, Zacharczuk M, Hetzler H, Haesler K, Fischer P (2022) Advancements on bifurcation behavior and operational deflection shapes of disk brake creep groan. J Sound Vib 534:116978. https://doi.org/10.1016/j.jsv.2022.116978
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
Alaaudeen KM, Aruna T, Ananthi G (2022) An improved strong tracking Kalman filter algorithm for real-time vehicle tracking. Materials Today: Proceedings 64:931–939. https://doi.org/10.1016/j.matpr.2022.02.507
Diaz M, Charbonnel PÉ, Chamoin L (2023) A new Kalman filter approach for structural parameter tracking: application to the monitoring of damaging structures tested on shaking-tables. Mech Syst Signal Process 182:109529. https://doi.org/10.1016/j.ymssp.2022.109529
Wang D, Löser M, Ihlenfeldt S, Wang X, Liu Z (2019) Milling stability analysis with considering process damping and mode shapes of in-process thin-walled workpiece. Int J Mech Sci 159:382–397. https://doi.org/10.1016/j.ijmecsci.2019.06.005
Kumar A, Parkash C, Vashishtha G, Tang H, Kundu P, Xiang J (2022) State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing. Reliab Eng Syst Saf 221:108356. https://doi.org/10.1016/j.ress.2022.108356
Shi Y, Li B, Au S-K (2022) Fast computation of uncertainty lower bounds for state-space model-based operational modal analysis. Mech Syst Signal Process 169:108759. https://doi.org/10.1016/j.ymssp.2021.108759
Gres S, Dohler M, Mevel L (2021) Uncertainty quantification of the modal assurance criterion in operational modal analysis. Mech Syst Signal Process 152:107457. https://doi.org/10.1016/j.ymssp.2020.107457
Jain S, Shukla S, Wadhvani R (2018) Dynamic selection of normalization techniques using data complexity measures. Expert Syst Appl 106:252–262. https://doi.org/10.1016/j.eswa.2018.04.008
Asnaashari E, Sinha JK (2014) Development of residual operational deflection shape for crack detection in structures. Mech Syst Signal Process 43(1–2):113–123. https://doi.org/10.1016/j.ymssp.2013.10.003
Song Q, Ju G, Liu Z, Ai X (2014) Subdivision of chatter-free regions and optimal cutting parameters based on vibration frequencies for peripheral milling process. Int J Mech Sci 83:172–183. https://doi.org/10.1016/j.ijmecsci.2014.04.002
Funding
The work was financially supported by the National Natural Science Foundation of China under Grant No. U20A20294, the Key Laboratory of Robotics and Intelligent Equipment of Guangdong Regular Institutions of Higher Education under Grant No. 2017KSYS009, and the DGUT Innovation Center of Robotics and Intelligent Equipment under Grant No. KCYCXPT2017006.
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Qiushuang Guo proposed the article’s innovative thinking, derived the core formula of the article and completed the English writing of the article. Xinyong Mao, Yili Peng, and Bin Li put forward many constructive suggestions for the writing of the whole article, while Rong Yan and Ling Yin put forward some constructive suggestions for the experimental part. Jianwen Liao performed some parts of the experimental validation.
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Appendix
Appendix
The derivation process of the modal contribution equation is provided here.
where \(M\) is the mass matrix of the thin-walled workpiece; \(C\) is the damping matrix; \(K\) is the stiffness matrix; \(Q\left(t\right)\) is the displacement vector; \(\dot{Q}\left(t\right)\) is the velocity vector; and \(\ddot{Q}\left(t\right)\) is the acceleration vector. To decompose each mode from the system vibration, modal decoupling is needed. There is a set of modal vectors \(\varphi =\left({\varphi }_{1}\text{, }{\varphi }_{2}...{\varphi }_{n}\right)\) in the modal space, where \(n\) is the modal order. Assuming that \(Q\left(t\right)=\varphi \cdot q\left(t\right)\), Eq. (18) can be expressed in modal space as:
where \(q\left(t\right)\) is the linear independence modal displacement vector and \({\varphi }^{T}\) is the transpose matrix of \(\varphi\). Based on the orthogonality of eigenvectors, Eq. (19) can be normalized as:
where \(I\) is the unit matrix, \(\Lambda\) is the diagonal matrix related to the damping ratio \({\xi }_{n}\) and modal frequency \({\omega }_{n}\), and \(L\) is the diagonal matrix related to the square of frequency \({\omega }_{n}^{2}\). To evaluate the effect of milling parameters on vibration, the cutting force \(F\left(t\right)\) is modeled from the orthogonal direction:
where \(d{F}_{tj}\) is the tangential microelement cutting force; \(d{F}_{rj}\) is the radial microelement cutting force; and \({K}_{tc}\) and \({K}_{rc}\) are the cutting force coefficients in relation to the shear component. The microelement equation for the milling force is:
Combining Eqs. (22) and (23), the expansion of the milling force can be obtained:
where \(g\left({\varphi }_{j}\right)\) is a window function to identify when the tool is in the cutting status, \(j\) is the sequence number of the tool tooth, and \({s}_{t}\) is the milling chip thickness, which can be expressed as:
where \({f}_{z}\) is the feed per tooth and \({\varphi }_{j}\left(z\right)\) is the instantaneous immersion angle. From Eqs. (20), (24), and (25), the following equation can be given:
where F (s, v, t) is the instantaneous cutting force related to the spindle speed \(s\), feed speed \(v\), and machining time t. The equation for a certain arbitrary \({n}_{th}\) mode is:
where \({F}_{n}\left(s,v,t\right)\) is the generalized force vector in modal coordinates and \({\varphi }_{n}^{T}\) is the transpose of the \({n}_{th}\) mode shape. The excitation force and modal displacement are converted into a complex exponential form:
where \({f}_{n}\left(s,v\right)\) is the amplitude of the input force; \({\eta }_{n}\) is the amplitude contributed by the \({n}_{th}\) mode in the vibration response signal; and \(\omega\) is the frequency of the input force. Equation (27) can be written as:
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Guo, Q., Mao, X., Peng, Y. et al. Online analysis method to correlate the mode shape for forced vibration in milling thin-walled workpieces. Int J Adv Manuf Technol 124, 329–347 (2023). https://doi.org/10.1007/s00170-022-10481-z
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DOI: https://doi.org/10.1007/s00170-022-10481-z