A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode

Vatandoost, Hossein; Alehashem, Seyed Masoud Sajjadi; Norouzi, M.; Taghavifar, Hamid; Ni, Yiqing

doi:10.1109/tmag.2019.2942804

Cited by 13 publications

(2 citation statements)

References 44 publications

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“…Yu et al designed an artificial neural network-based model with three input neurons, 18 hidden neurons and one output neuron, to predict the MRE isolator behavior under various loading conditions (Yu et al, 2015). Vatandoost et al utilized a multilayer perceptron-based feed-forward neural network with backpropagation training technique to characterize MRE's dynamic behavior in tension-compression mode (Vatandoost et al, 2019). Saharuddin et al used two basic neural network models, that is, artificial neural network and extreme learning machine, to predict the magnetic field dependent-stiffness and damping properties of MRE (Saharuddin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Modeling and semi-active control performance analysis of MRE isolator based on BPNN optimized by GA using different evaluation indexes

Wang

Huang

Liu

et al. 2023

Journal of Intelligent Material Systems and Structures

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The utilization of isolation systems employing Magnetorheological Elastomer (MRE) devices holds significant promise for structural vibration applications due to their customizable stiffness and damping characteristics. However, the nonlinear dynamics inherent in MRE isolators present formidable obstacles for the establishment of accurate models and development of effective control strategies for practical implementation. In this work, the dynamic properties of a self-made MRE isolator under different loading conditions are tested and analyzed. Then the nonparametric forward model and inverse model of MRE isolator based on BPNN (back propagation neural network) are established respectively, and the GA (genetic algorithm) is used to optimize the neural structure of BPNN. The precision and accuracy of the forward and inverse model is verified by comparing the predicted and experimental data. Simulation and experimental results show that the BPNN optimized by GA can efficiently and accurately model the nonlinear behavior of MRE isolators. Based on this, we take an eight-story shear frame building based on the proposed MRE vibration isolator model as the research object and numerically studied the vibration suppression effect under the control of three typical control algorithms, that is, LQR, FC, and FC-PID. In the process of evaluating the effect of vibration isolation control, in addition to utilizing the traditional displacement amplitude, layer distance and acceleration as evaluation indexes, we also propose a new comprehensive evaluation index which has weighting coefficients and considers the input cost. It is shown that the newly proposed comprehensive index can more conveniently compare the advantages and disadvantages of different control algorithms, and the fuzzy PID is the most suitable among the three control algorithms.

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Section: Introductionmentioning

confidence: 99%

Modeling and semi-active control performance analysis of MRE isolator based on BPNN optimized by GA using different evaluation indexes

Wang

Huang

Liu

et al. 2023

Journal of Intelligent Material Systems and Structures

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“…The magnetizable particles can be either distributed homogeneously (isotropic) or in a chain-like manner (anisotropic) within the matrix (Fan et al, 2019). The MREs are, thus, considered to offer meritorious potentials for many engineering applications, particularly for vibration and noise reduction; thanks to their wide variations in stiffness and energy absorption properties, apart from the low response time, stability, compatibility with mechanical components and reasonably low power requirement (Li et al, 2014; Vatandoost et al, 2019). A number of studies have also demonstrated macroscopic applications of MREs such as vibration isolator for highway bridges (Yarra et al, 2018), vehicle seat suspensions (Du et al, 2011), powertrain mounts (Xin et al, 2016b), adaptive tuned vibration absorbers (ATVAs) (Qian et al, 2017; Sun et al, 2017), structural seismic mitigation as well microscopic applications such as force sensors (Li et al, 2009), soft actuator (Kashima et al, 2012), sealing eye retina detachments (Alekhina et al, 2018), MRE-based stiffness display (Hooshiar et al, 2020), and artificial lymphatic vessels (Behrooz, 2015).…”

Section: Introductionmentioning

confidence: 99%

A novel bi-directional shear mode magneto-rheological elastomer vibration isolator

Jalali

Dianati

Norouzi

et al. 2020

Journal of Intelligent Material Systems and Structures

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In this article, a novel bi-directional shear mode magneto-rheological elastomer–based vibration isolator has been designed, fabricated, and characterized to improve the dynamic response and identification of this class of “intellectual” mechanical devices. A heuristic embodiment has been realized in order to design such an isolator wherein both the vertical and horizontal directions can be operated only in the shear mode not only individually but also simultaneously. Two fixtures have been designed for performing the characterization of the magneto-mechanical behavior of the proposed magneto-rheological elastomer isolator in the vertical and horizontal shear modes under wide ranges of strain amplitude (4%–32%), excitation frequency (1–8 Hz), and magnetic flux density (0–220 mT). Experimental results revealed maximum relative magneto-rheological effects of 35% and 27 % in the vertical and horizontal shear modes, respectively. Furthermore, basic mathematical models of single-degree-of-freedom systems, employing the magneto-rheological elastomer–based isolator in the vertical and horizontal shear modes, have been established. The proposed magneto-rheological elastomer isolator in the vertical mode exhibited natural frequency shift of 6.1% by a small increment in the magnetic flux density which approves the potential of the proposed bi-directional shear mode magneto-rheological elastomer–based vibration isolator for vibration control applications, such as seat suspension systems.

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A review on magneto-mechanical characterizations of magnetorheological elastomers

Bastola

Hossain

2020

Composites Part B: Engineering

240

122

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode

Cited by 13 publications

References 44 publications

Modeling and semi-active control performance analysis of MRE isolator based on BPNN optimized by GA using different evaluation indexes

Modeling and semi-active control performance analysis of MRE isolator based on BPNN optimized by GA using different evaluation indexes

A novel bi-directional shear mode magneto-rheological elastomer vibration isolator

A review on magneto-mechanical characterizations of magnetorheological elastomers

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