Analysis on Fatigue Damage of Metal Rubber Vibration Isolator

Cao, Feng; Bai, Hong Bai; Yang, Jian Chun; Ren, Guo Quan

doi:10.4028/www.scientific.net/amr.490-495.162

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…To further explore the performance of the vibration isolator, many theoretical and experimental studies have been conducted. Cao et al [12] studied the fatigue damage characteristics of metal rubber isolators. Yan et al [13] designed a new type of shape memory alloy metal rubber (SMA-MR) damper and investigated the law of mechanical properties with temperature through experimental methods.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Dynamic Performance of an All-Metallic Vibration Isolator by Elliptic Method and Frequency Sweeping Method

Zou

Zheng

et al. 2022

Symmetry

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A horizontally symmetric all-metallic vibration isolator (AM-VI) is proposed to further investigate the dynamic mechanical performance. The novel AM-VI was constructed by combining hat-shaped metal rubber and oblique springs, which were connected in parallel. The springs were arranged symmetrically relative to the support. The elliptic method and the frequency sweeping method were used to compare the dynamic stiffness and the loss factor of the AM-VI. The results demonstrated that the dynamic stiffness and the loss factor calculated by two distinct test methodologies were considerably different, indicating that the inertial force effect of the dynamic testing equipment should be taken into count when adopting the elliptic method. Furthermore, when the vibration isolation performance was evaluated by utilizing mechanical impedance and force transmissibility, the AM-VI achieved excellent vibration isolation performance within a broad frequency range.

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Section: Introductionmentioning

confidence: 99%

Comparison of Dynamic Performance of an All-Metallic Vibration Isolator by Elliptic Method and Frequency Sweeping Method

Zou

Zheng

et al. 2022

Symmetry

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“…3(f). Cao et al [20] established a mathematical model for simulating the material loading-unloading mechanical properties founded on singlewire spiral composed of variable-length curved beams.…”

Section: Introductionmentioning

confidence: 99%

Study on Multi-Point Random Contact Characteristics of Metal Rubber Spiral Mesh Structure

et al. 2019

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Metal rubber (MR) is a porous damping material, which achieves the damping energy consumption by the contact friction between the internal wires. Complex wire structure makes explanation of energy dissipation mechanism by the traditional theoretical models limited to comparison with equivalent models or microcell models, which cannot truly reflect the spatial multi-point contact characteristics of each wire. This work demonstrates numerical modeling based on actual preparation process parameters of annular MR. Using the penalty function to solve the complex contact that is difficult to predict between the internal spiral wires, the variation laws for equivalent stress and strain recorded during the loading-unloading process of MR, were obtained. The small-ball algorithm and the Tabu search algorithm were used to realize the accurate assessment of contact points between the wires and the hyper dimension matrix was used to track the friction state of the contact points in real time, thereby obtaining a proportional relationship between various friction forms during the loading-unloading process. It is found that sliding friction accounts for the majority of the total number of contact points, which is about 81.38%, consistent with the equivalent plastic strain law of stepwise change. It becomes further clarified from the microscopic point that MR accomplishes macroscopic energy dissipation mechanism through the fretting slip friction between the internal wires. In order to verify the authenticity of the simulation, a quasi-static loading-unloading experiment of MR was carried out under identical parameters, and the experimental macroscopic results were in excellent agreement with the simulation results. The research indicates that the MR finite element model established in this paper can precisely describe the dynamic contact of the internal structural features of MR materials, for providing a theoretical basis for guiding the preparation and application of MR.INDEX TERMS MR, finite element model, dynamic contact, fretting slip Friction, damping energy consumption.

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“…The support vector machine (SVM) algorithm was originally designed as a classifier [18] but may also be used for regression, SVR, and feature selection [19]. In classification, SVM determines the optimal separating hyperplane between linearly separable classes maximizing the margin, which is defined as the distance between the hyperplane and the closest points on both sides (the support vectors).…”

mentioning

confidence: 99%

Prediction of the Transition-Temperature Shift Using Machine Learning Algorithms and the Plotter Database

Ferreño

Serrano

Kirk

et al. 2022

Metals

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The long-term operating strategy of nuclear plants must ensure the integrity of the vessel, which is subjected to neutron irradiation, causing its embrittlement over time. Embrittlement trend curves used to predict the dependence of the Charpy transition-temperature shift, ΔT41J, with neutron fluence, such as the one adopted in ASTM E900-15, are empirical or semi-empirical formulas based on parameters that characterize irradiation conditions (neutron fluence, flux and temperature), the chemical composition of the steel (copper, nickel, phosphorus and manganese), and the product type (plates, forgings, welds, or so-called standard reference materials (SRMs)). The ASTM (American Society for Testing and Materials) E900-15 trend curve was obtained as a combination of physical and phenomenological models with free parameters fitted using the available surveillance data from nuclear power plants. These data, collected to support ASTM’s E900 effort, open the way to an alternative, purely data-driven approach using machine learning algorithms. In this study, the ASTM PLOTTER database that was used to inform the ASTM E900-15 fit has been employed to train and validate a number of machine learning regression models (multilinear, k-nearest neighbors, decision trees, support vector machines, random forest, AdaBoost, gradient boosting, XGB, and multi-layer perceptron). Optimal results were obtained with gradient boosting, which provided a value of R2 = 0.91 and a root mean squared error ≈10.5 °C for the test dataset. These results outperform the prediction ability of existing trend curves, including ASTM E900-15, reducing the prediction uncertainty by ≈20%. In addition, impurity-based and permutation-based feature importance algorithms were used to identify the variables that most influence ΔT41J (copper, fluence, nickel and temperature, in this order), and individual conditional expectation and interaction plots were used to estimate the specific influence of each of the features.

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Analysis on Fatigue Damage of Metal Rubber Vibration Isolator

Cited by 7 publications

References 2 publications

Comparison of Dynamic Performance of an All-Metallic Vibration Isolator by Elliptic Method and Frequency Sweeping Method

Comparison of Dynamic Performance of an All-Metallic Vibration Isolator by Elliptic Method and Frequency Sweeping Method

Study on Multi-Point Random Contact Characteristics of Metal Rubber Spiral Mesh Structure

Prediction of the Transition-Temperature Shift Using Machine Learning Algorithms and the Plotter Database

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