FRF based joint dynamics modeling and identification

Mehrpouya, Majid; Graham, Eldon; Park, Simon S.

doi:10.1016/j.ymssp.2013.03.022

Cited by 61 publications

(23 citation statements)

References 23 publications

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“…In order to fully illustrate the information on the measured data, the prior distribution is assumed to be a uniform distribution. Using equation (13) with D = 0:15, the 'measured' displacement PSD of the 'measured point' with 15% noise level is generated at first. Figure 3 presents 'measured' displacement PSD in the frequency range of 0-60 Hz where 1200 frequency points are regularly distributed.…”

Section: Numerical Examplementioning

confidence: 99%

Bayesian identification of bolted-joint parameters using measured power spectral density

Zhang

Zhao

Yunyun

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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A Bayesian method for the optimal estimation of parameters that characterize a bolted joint based on measured power spectral density is proposed in this article. Due to uncertainties such as measurement noise and modelling errors, it is difficult to identify joint parameters of a bolted structure accurately with incomplete measured response data. In this article, using the Bayesian probability framework to describe the uncertainty of the joint parameters and using the power spectrum of the structural response of the single-point/multi-point excitation as measurements, the conditional probability density function of the joint parameters is established. Then, the Bayesian maximum posterior estimation is performed by an optimization method. Two simplified bolted-joint models are built in the numerical examples. First, the feasibility of the proposed method in the undamped model is proved. Then, taking advantage of multi-point excitation, the identification accuracy of the proposed method in the damped model is improved. The numerical results show that the proposed method can accurately identify the stiffness and damping characteristics of joint parameters with good robustness to noise. Finally, the joint parameters of the finite element model for an aero-engine casing are identified by the proposed method with satisfactory accuracy.

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Section: Numerical Examplementioning

confidence: 99%

Bayesian identification of bolted-joint parameters using measured power spectral density

Zhang

Zhao

Yunyun

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…(8) in Eq. (14) and also accounting for the compatibility conditions of Eq. (16), the union between the subsystems A and AB 2 can be written in compact matrix form as: …”

Section: Substructure Couplingmentioning

confidence: 99%

“…However, sometimes the reverse problem of decoupling becomes necessary. Examples of decoupling include extraction of rotational FRFs at coupling points using the inverse RCSA techniques [9,14]. 3 Decoupling to extract dynamics of the complete machine that are to be subsequently coupled to other substructures, as is considered in this paper, is new.…”

Section: Introductionmentioning

confidence: 99%

Predicting mobile machine tool dynamics by experimental dynamic substructuring

Law

Rentzsch

Ihlenfeldt

2016

International Journal of Machine Tools and Manufacture

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“…The substructures are connected to each other by joints, which may result in the discontinuity of the mechanical structure in practice. The complex joint behaviors influence the properties of the whole machine significantly (Mehrpouya et al, 2013;Gao et al, 2015). Actually the dynamic stiffness of the assembled structure is highly dependent on the joint stiffness, which takes up 60-80% of the entire dynamic stiffness (Burdekin et al, 1979) and causes 40-60% deformations (Vafaei et al, 2002) generally.…”

Section: Introductionmentioning

confidence: 99%

Research on modeling and identification of machine tool joint dynamic characteristics

Yin

Dong

Yin

et al. 2019

JAMDSM

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The dynamic characteristics of a joint affect the machine tool operations notably. In this paper, an improved approach is proposed to identify the dynamic stiffness of the joint and to construct a relevant dynamic model. The theoretical dynamic model assembled of two beams is built using the frequency response functions. The identification formulas are derived based on the mechanical equilibrium condition and the displacement compatibility condition to describe the relationship between the assembled structures and substructures. Then an inconsistent equation containing the identification relationship is developed. The equivalent value of the dynamic stiffness is extracted employing the least square method. In the identification process, a part of frequency response functions, which is difficult to be measured, is considered as an intermediate variable to avoid introducing errors. According to the results, the calculated dynamic responses of the assembly show better agreement with the experimental measured data in comparison with the results derived from a typical method, which validates the feasibility. And the results demonstrate higher accuracy of the proposed method.

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FRF based joint dynamics modeling and identification

Cited by 61 publications

References 23 publications

Bayesian identification of bolted-joint parameters using measured power spectral density

Bayesian identification of bolted-joint parameters using measured power spectral density

Predicting mobile machine tool dynamics by experimental dynamic substructuring

Research on modeling and identification of machine tool joint dynamic characteristics

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