Volume 19, Issue 8 (2019)                   Modares Mechanical Engineering 2019, 19(8): 1917-1928 | Back to browse issues page

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Fallah M, Moetakef-Imani B. Identification of Dynamic Model for an Active Boring Bar. Modares Mechanical Engineering. 2019; 19 (8) :1917-1928
URL: http://igtc12.modares.ac.ir/article-15-25964-en.html
1- Mechanical Engineering Department, Engineering Faculty, Ferdowsi University of Mashhad, Mashhad, Iran
2- Mechanical Engineering Department, Engineering Faculty, Ferdowsi University of Mashhad, Mashhad, Iran , imani@um.ac.ir
Abstract:   (762 Views)
In this paper, a novel dynamic model is proposed for an actively damped boring bar equipped with electromagnetic actuator. The dynamic models of actuator and boring bar are obtained by using the suggested systematic identification approach, which is based upon the fundamental tools and techniques of system identification theory. The electro-mechanical system or the forward path is consisted of 3 basic components, i.e. linear power amplifier, electrodynamic shaker, and boring bar structure. In this paper, the dynamic models of forward path’s sub-systems are simultaneously identified. The component-based identification approach has led to a remarkable finding about the source of nonlinearity in the dynamic model of forward path. According to the presented experimental observations, it has been concluded that electromagnetic actuator can be modeled as a linear dynamic system, while the boring bar structure exhibits nonlinear behavior, since the prediction accuracy of boring bar dynamic model is drastically reduced by changing the amplitude of excitation. As a result, a new parameter varying dynamic model is presented for describing the dynamic behavior of forward path in terms of both frequency and excitation level. The proposed dynamic model has a predefined representation with the least possible mathematical order. It can anticipate the time domain response of forward path due to chirp excitation with 88% accuracy. In addition, during the validation stage, the proposed model forecasts the dynamic response of system due to Gaussian white noise excitation with remarkable accuracy. Moreover, the dynamic model of electromagnetic actuator can predict the dynamic force signature of actuator with 85% accuracy.
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Received: 2018/10/9 | Accepted: 2019/01/26 | Published: 2019/08/12

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