Actively lubricated hybrid journal bearings based on magnetic fluids for high-precision spindles of machine tools

Urreta, Harkaitz; Aguirre, Gorka; Kuzhir, Pavel; Lacalle, Luis Norberto López de

doi:10.1177/1045389x19862358

Cited by 38 publications

(19 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, magnetorheological fluids (MR fluids) as one of the smart materials has been attracting the attention of many scholars [ 33 , 34 ]. Lapping or polishing of the hard and brittle materials such as optical glass can be realized by adding a reasonable concentration of abrasive particles in MR fluids with an MR effect.…”

Section: Methodsmentioning

confidence: 99%

Study on Material Removal Model by Reciprocating Magnetorheological Polishing

Wang¹,

Xiu

Sun

et al. 2021

Micromachines

View full text Add to dashboard Cite

In this study, a new reciprocating magnetorheological polishing (RMRP) method for a flat workpiece was proposed. Based on the RMRP principle and Preston equation, the material removal rate (MRR) model of the RMRP as well as its normal polishing pressure model was established. On this basis, the effects of different technological parameters including workpiece rotation speed, eccentric wheel rotation speed and eccentricity on the MRR of the workpiece were investigated. The K9 optical flat glass was polished with the RMRP setup to verify the MRR model. The experimental results showed that the effect of workpiece rotation speed on the MRR was much greater than that of eccentric wheel rotation speed and eccentricity, and the MRR increased from 0.0115 ± 0.0012 to 0.0443 ± 0.0015 μm/min as workpiece rotation speed rose. The optimum surface roughness reduced to Ra 50.8 ± 1.2 from initial Ra 330.3 ± 1.6 nm when the technical parameters of the workpiece rotation speed of 300 rpm, the eccentric wheel rotation speed of 20 rpm and the eccentricity of 0.02 m were applied. The average relative errors between the theoretical and experimental values were 16.77%, 10.59% and 7.38%, respectively, according to the effects of workpiece rotation speed, eccentric wheel rotation speed and eccentricity on MRR.

show abstract

Section: Methodsmentioning

confidence: 99%

Study on Material Removal Model by Reciprocating Magnetorheological Polishing

Wang¹,

Xiu

Sun

et al. 2021

Micromachines

View full text Add to dashboard Cite

show abstract

“…The electromagnetic bearing and its working principle are shown in Fig 1. As shown in Fig 1, the electromagnets are symmetrically fixed, and the rotor is placed between the electromagnets. If there is no external force, the relative electromagnetic force generated by the two electromagnets is the same after energization, and the rotor will be suspended in the air, without any touch to the inner wall of the bearing [14]. The displacement state of the rotor is monitored by a monitoring probe, and the displacement signal is transmitted to the controller.…”

Section: Hybrid Electromagnetic and Elastic Foil Gas Bearingmentioning

confidence: 99%

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Sun

2020

PLoS ONE

View full text Add to dashboard Cite

The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. The related principles and structure of hybrid electromagnetic and elastic foil gas bearings is introduced firstly. Then, the proportional, integral, and derivative (PID) bearing controller is introduced and improved into two controllers: IPD and CPID. The controllers and hybrid bearing system are controlled based on the RBF neural network based on deep learning. The characteristics of the hybrid bearing system are explored at the end of this study, and the control simulation research is developed based on the Simulink simulation platform. The effects of the PID, IPD, and CIPD controllers based on the RBF neural network are compared, and they are also compared based on the traditional particle swarm optimization (PSO). The results show that the thickness, spread angle, and rotation speed of the elastic foil have great impacts on the bearing system. The proposed CIPD bearing control method based on RBF neural network has the shortest response time and the best control effect. The controller parameter tuning optimization starts to converge after one generation, which is the fastest iteration. It proves that RBF neural network control based on deep learning has high feasibility in hybrid bearing system. Therefore, the results provide an important reference for the application of deep learning in rotating machinery.

show abstract

“…Thus, it is extremely important to monitor the health state and diagnose the early fault. In the past few decades, the issue of bearing fault diagnosis has attracted more and more attentions, and many methods are proposed in view of vibration, acoustics, liquid and deep learning [2][3][4][5]. Moreover, fault signals are always shown to be weak, especially in early stages.…”

Section: Introductionmentioning

confidence: 99%

A New Dynamical Method for Bearing Fault Diagnosis Based on Optimal Regulation of Resonant Behaviors in a Fluctuating-Mass-Induced Linear Oscillator

Chen

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

Stochastic resonance (SR), a typical randomness-assisted signal processing method, has been extensively studied in bearing fault diagnosis to enhance the feature of periodic signal. In this study, we cast off the basic constraint of nonlinearity, extend it to a new type of generalized SR (GSR) in linear Langevin system, and propose the fluctuating-mass induced linear oscillator (FMLO). Then, by generalized scale transformation (GST), it is improved to be more suitable for exacting high-frequency fault features. Moreover, by analyzing the system stationary response, we find that the synergy of the linear system, internal random regulation and external excitement can conduct a rich variety of non-monotonic behaviors, such as bona-fide SR, conventional SR, GSR, and stochastic inhibition (SI). Based on the numerical implementation, it is found that these behaviors play an important role in adaptively optimizing system parameters to maximally improve the performance and identification ability of weak high-frequency signal in strong background noise. Finally, the experimental data are further performed to verify the effectiveness and superiority in comparison with traditional dynamical methods. The results show that the proposed GST-FMLO system performs the best in the bearing fault diagnoses of inner race, outer race and rolling element. Particularly, by amplifying the characteristic harmonics, the low harmonics become extremely weak compared to the characteristic. Additionally, the efficiency is increased by more than 5 times, which is significantly better than the nonlinear dynamical methods, and has the great potential for online fault diagnosis.

show abstract

Actively lubricated hybrid journal bearings based on magnetic fluids for high-precision spindles of machine tools

Cited by 38 publications

References 29 publications

Study on Material Removal Model by Reciprocating Magnetorheological Polishing

Study on Material Removal Model by Reciprocating Magnetorheological Polishing

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

A New Dynamical Method for Bearing Fault Diagnosis Based on Optimal Regulation of Resonant Behaviors in a Fluctuating-Mass-Induced Linear Oscillator

Contact Info

Product

Resources

About