Hysteresis modeling of clamp band joint with macro-slip

Friction is a nonlinear effect that occurs in all mechanical systems which may cause limit cycles, tracking errors, and other undesirable effects. Traditional static friction models cannot characterize all the friction situations. In recent years, neural network (NN) technique has been widely used to approximate the nonlinear function. In this paper, a new method which combines radical basis function neural network (BRFNN) with beetle antennae search (BAS) algorithm for modeling friction in a robotic joint is proposed. Velocity, load, and temperature are considered as the three factors that influence the static friction. It is shown that the proposed BAS-RBFNN possesses better performance in terms of faster convergence rate and higher accuracy.

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“…erefore, a precise friction model may considerably improve the efficiency for control purposes and other applications [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

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“…Equations (6), (10), and (25) can be combined to yield the following thermal model for an arbitrary multi-pulley belt drive system:…”

Section: Overall Structurementioning

confidence: 99%

“…Furthermore, the power loss is influenced by several factors, one of which is the belt pressure on the pulley. Some authors reported on an analytical model [10] for the friction damping of round clamp band joints, which can be used to predict the energy dissipation caused by the friction contact between the joint components. The belt pressure on the pulley also leads to a change in the axial force of the pulley, and some experiments indicated that this force influences the power transmission efficiency, although this influence is not significant [11].…”

Section: Introductionmentioning

confidence: 99%

Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System

Liu

Behdinan

2020

Applied Sciences

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The serpentine belt drive system is used in the auto industry. To avoid thermal destruction inside the belt drive and improve the thermal fatigue life of pulley materials under a variety of operating conditions, the temperature information for each load case must be determined within only a few seconds. To this end, this paper proposes an advanced thermal model to calculate the temperature distribution of a serpentine belt drive at static state operating conditions in an efficient manner. In this model, using analytical and numerical methods, a set of equations is developed according to the thermal flows and heat exchanges occurring in the system. After calculating the thermal flows of each pulley and the belt temperature, the baseline numerical simulations are modified to output the temperature distribution for each pulley. In this manner, the time-consuming numerical calculations for each pulley are performed only once and then analytically modified to provide the temperature predictions for various designed load cases, which dramatically reduces the computational time while maintaining the accuracy. Furthermore, experiments were performed to obtain the temperature data, and the results exhibited a good agreement with the corresponding calculated results. The proposed model can thus be effectively utilized for several types of belt systems and the material development of pulleys.

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“…Simulations. In this paper, the multibody program ADAMS is adopted to consider the system dynamic properties and derive the dynamic tension in the belts for system reliability assessment. For different components in a belt drive system, the system dynamic equation can be derived by the Lagrange equation as follows [11][12][13]:…”

Section: Problems In Solution Of Dynamic Stress Via Analyticalmentioning

confidence: 99%

Reliability and Lifetime Distribution Analysis of Belt Drive Systems considering Time‐Dependent Stiffness of Belts

Gao

Xie

2018

Shock and Vibration

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Time-dependent reliability models, failure rate models, and lifetime distribution models of belt drive systems are developed in this paper, which take geometric parameters, material parameters, and motion parameters as the input and consider the dynamic properties of the belt drive systems. Most of the reliability models of belt drive systems are static models. The proposed models can take into account the time-dependent statistical properties of dynamic stress process. Moreover, the stiffness degradation is considered in the established models, whose influences on reliability indices are analyzed in case study. The results show that stiffness degradation has significant impacts on reliability, failure rate, and lifetime distribution. In addition, sensitivity models of reliability with respect to input parameters are constructed, which can be used for the guidance of safe design of belt drive systems.

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Hysteresis modeling of clamp band joint with macro-slip

Cited by 24 publications

References 16 publications

An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint

An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint

Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System

Reliability and Lifetime Distribution Analysis of Belt Drive Systems considering Time‐Dependent Stiffness of Belts

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