Dynamic modeling and parameters identification of a spindle–holder taper joint

Xu, Chao; Zhang, Chenglong; Wu, Zhijun; Yu, Dingwen; Feng, Pingfa

doi:10.1007/s00170-012-4586-1

Cited by 28 publications

(10 citation statements)

References 21 publications

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“…Agapiou [11] modeled the spindle-holder taper interface as a linear spring together with a rotational spring, and the parameters were determined by matching the experiment data to the FEM results. Xu et al [12] modeled the taper interface as two pairs of radial spring and one axial spring, and proposed a rigid-body dynamics model for the identification of contact parameters. Özşahin et al [13] presented closed-form expressions for the identification of dynamical contact parameters in spindle-holder-tool assemblies.…”

Section: Introductionmentioning

confidence: 99%

“…With the above theoretical and experimental methods for the spindle-holder interface, the effects of clamping force on the contact characteristics of taper interface have been extensively studied [1,2,[7][8][9][10][11][12]18]. In contrast, few researches focused on the angle fitting error of taper joint, although it has an important influence.…”

Section: Introductionmentioning

confidence: 99%

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Ultrasonic measurement of contact stiffness and pressure distribution on spindle–holder taper interfaces

Zhang

et al. 2015

International Journal of Machine Tools and Manufacture

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ultrasonic measurement of contact stiffness and pressure distribution on spindle–holder taper interfaces

Zhang

et al. 2015

International Journal of Machine Tools and Manufacture

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“…Aiming at the dynamic modeling and parameters identification of joints for machine tools, many researchers have focused on three kinds of methods: theoretical calculation method, experimental method and the method combining theory with experiment. 6,13 However, due to the complexity and lower accuracy of the theoretical calculation method, and the experimental method has poor universality which can only identify a single dynamic parameter of the joint, therefore, the method combining theory with experiment is widely adopted at present. For this method, the dynamic model with joints' parameters is established based on modeling theories; the experimental results of the joints-system are obtained under certain conditions; then the parameters of joints can be effectively identified by adopting the optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

Zhang

Liu

Huang

et al. 2021

Advances in Mechanical Engineering

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Dynamic parameters of joints are indispensable factors affecting performance of machine tools. In order to obtain the stiffness and damping of sliding joints between the working platform and the machine tool body of the surface grinder, a new method of dynamic parameters identification is proposed that based on deep neural network (DNN) modeling. Firstly, the DNN model of dynamic parameters for working platform-machine tool body sliding joints is established by taking the stiffness and damping parameters as the input and the natural frequencies as the output. Secondly, the number of hidden layers in DNN topology is optimally selected in order to the optimal training results. Thirdly, combining the predicted results by DNN model with experimental results by modal test, the stiffness and damping are identified via cuckoo search algorithm. Finally, the relative error between the predicted and experimental results is less than 2.2%, which achieves extremely high prediction precision; and thereby indicates the feasibility and effectiveness of the proposed method.

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“…The clamping force influences the dynamic characteristics of the tool holder significantly. 25,26 Thus, the dynamic characteristics of the spindle tool holder joint must be described clearly before the spindle system can be modelled.…”

Section: Introductionmentioning

confidence: 99%

Dynamics prediction of spindle system using joint models of spindle tool holder and bearings

Zhang

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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Spindle system design requires the simulation of dynamic characteristics with consideration of joint characteristics. A spindle system modelling method using a spindle tool holder and bearing joints was presented for the prediction of dynamic behaviours. A substructure receptance coupling method was used to model the joints of the spindle tool holder and bearings, and the identification method of joint parameters was developed. The parameters of spindle tool holder joint were identified from the experimental platform based on the distributed model, and the parameters of the bearing joints were obtained by the Hertzian contact. Timoshenko beams were used to describe the spindle system components. The modal and harmonic response analysis was performed to predict the dynamic characteristics of the spindle system. The prediction results of natural frequency and the dynamic response were validated by impact testing. The model showed a good match for predicting the dynamic characteristics of the spindle system. The presented modelling method is useful in the performance evaluation of spindle system.

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Dynamic modeling and parameters identification of a spindle–holder taper joint

Cited by 28 publications

References 21 publications

Ultrasonic measurement of contact stiffness and pressure distribution on spindle–holder taper interfaces

Ultrasonic measurement of contact stiffness and pressure distribution on spindle–holder taper interfaces

Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

Dynamics prediction of spindle system using joint models of spindle tool holder and bearings

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