Improved thermal resistance network model of motorized spindle system considering temperature variation of cooling system

Yang, Liu; Ma, Yaxin; Meng, Qingyu; Xin, Xicheng; Ming, Shuaishuai

doi:10.1007/s40436-018-0239-4

Cited by 25 publications

(18 citation statements)

References 15 publications

(13 reference statements)

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“…, (34) where Kij and Koj denote the load deformation coefficients in the bearing; αij and αoj are the contact angles between ball j and the inner raceway or outer raceway, respectively; Mgj and Fcj represent the gyroscopic moment and centrifugal force on ball j, respectively.…”

Section: Computation Methods Of Heatmentioning

confidence: 99%

“…Qimax and Qomax can be determined by simultaneously solving Eqs. (31)(32)(33)(34)(35)(36)(37)(38)(39).…”

Section: Computation Methods Of Heatmentioning

confidence: 99%

“…In summary, to acquire the friction heat of bearings, the contact load is required by solving Eqs. (31)(32)(33)(34)(35)(36)(37)(38)(39). The contact load of bearings is closely related to the temperature field of the system and the thermal deformation of bearings.…”

Section: Computation Methods Of Heatmentioning

confidence: 99%

“…Furthermore, the influence of cooling system on the temperature variation of motorized system has captured the attention of researchers. Liu et al [34] developed an improved thermal resistance network model by considering the temperature variation of cooling system and analyzed the effects of coolant flow rate and speed on the temperature variation of the spindle system. The results indicated that the coolant flow rate has a considerable effect on the temperature of bearings at high rotation speeds.…”

mentioning

confidence: 99%

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Thermal network model and experimental validation for a motorized spindle including thermal–mechanical coupling effect

Zhou

et al. 2021

Int J Adv Manuf Technol

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Thermal deformation caused by temperature rise have an influence on the dynamic performance of a motorized spindle. In turn, the change in the dynamic performance will affect the temperature rise and thermal deformation of the system. However, the latter was rarely focused on in the previous literature. Therefore, a thermal network model of motorized spindle is enhanced by considering the thermal-mechanical coupling effect. Then, an iterative method is presented to solve the coupled equations, and a temperature test rig of the motorized spindle is set up to verify the proposed model. The relative error between the predicted and experimental results at two test points decreases by 9.56% and 3.44% after considering the thermal-mechanical coupling effect. The comparison with the experimental results shows that the proposed model with thermal-mechanical coupling effect can obtain a more accurate temperature field than the previous model. Key words: Motorized spindle; Thermal network model; Thermal-mechanical coupling; Temperature field IntroductionMotorized spindle is a core functional component of computer numerical control (CNC). High transmission accuracy is implemented by the spindle system because the spindle is driven by the motor directly. However, the thermal deformation of the spindle system, caused by uneven temperature rise, reduces machining precision and reliability of a motorized spindle [1]. Temperature affects not only the execution accuracy of the mechanical system [2], but also the machining accuracy of the machine tool [3]. To compensate for the error induced by the thermal deformation, an accurate temperature field prediction model of the motorized spindle is essentially studied.The temperature rise of the spindle system usually focuses on thermal characteristics. To date, considerable efforts have been dedicated on the thermal characteristics of the spindle system. As the bases of calculation for the temperature in the spindle system, a thermal-dynamic model of bearings was established by Palmgren [4] and the frictional heat of bearings was calculated by Jones [5]. Thereafter, the thermal characteristics of the spindle system have attracted increasing attention. The temperature in the spindle system was determined through the finite difference model (FDM) by Harris [6], but the thermal deformation of the spindle system was not included in their model. Later, a thermal characteristic model with thermal deformation was developed [7]. The finite element method (FEM) was also used to predict the temperature field of the spindle system [8][9][10].Recently, motorized spindles as a core part of CNC have been concerned. A finite difference model with heat transfer mechanism of motorized spindle was proposed by Bossmanns [11], and the temperature at several locations matched the measured values. Chen et al. [12] developed a thermal error model of motorized spindle and found that spindle temperature has the characteristics of time-varying, nonlinearity, and strong dependence on rotation speed. Affected by...

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Section: Computation Methods Of Heatmentioning

confidence: 99%

“…Qimax and Qomax can be determined by simultaneously solving Eqs. (31)(32)(33)(34)(35)(36)(37)(38)(39).…”

Section: Computation Methods Of Heatmentioning

confidence: 99%

Section: Computation Methods Of Heatmentioning

confidence: 99%

mentioning

confidence: 99%

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Thermal network model and experimental validation for a motorized spindle including thermal–mechanical coupling effect

Zhou

et al. 2021

Int J Adv Manuf Technol

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“…The PBM builds the relationship between thermal errors and heat generated by a system of nonlinear differential equations, which frequently employs the principles of friction-induced heat, heat conduction, heat convection, heat radiation, and thermal expansion in the model construction. Liu et al [4] established a thermal resistance network model of the motorized spindle system of a CNC machining tool. The influences of the radial and axial thermal conduction resistance and thermal convection resistance between the cooling system and the components of the spindle system on the temperature rise are considered in the model.…”

Section: Introductionmentioning

confidence: 99%

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

Liu

et al. 2021

Adv. Manuf.

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The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry. Among all errors, thermal error affects the machining accuracy considerably. Because of the significant impact of Industry 4.0 on machine tools, existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data. A thermal error modeling method is proposed based on bidirectional long short-term memory (BiLSTM) deep learning, which has good learning ability and a strong capability to handle a large group of dynamic data. A four-layer model framework that includes BiLSTM, a feedforward neural network, and the max pooling is constructed. An elaborately designed algorithm is proposed for better and faster model training. The window length of the input sequence is selected based on the phase space reconstruction of the time series. The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting. The average depth variation of the workpiece was reduced from approximately 50 µm to less than 2 µm after compensation. The reduction in maximum depth variation was more than 85%. The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

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Improving the Thermal Behavior of High-Speed Spindles Through the Use of an Active Controlled Heat Pipe System

Jonath,

Luderich,

Brezina

et al. 2023

Lecture Notes in Production Engineering

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The thermo-elastic behavior of high-speed spindles has a significant influence on the machine accuracy. The Tool Center Point (TCP) changes continuously, not only due to the different temperature levels and energy inputs during warm-up, full-load and part-load operation, but also during interruptions for workpiece or tool changes. In this paper a heat pipe based tempering system is presented to control the spindle temperature and thus to keep the TCP displacement at a constant level, regardless of speed and load. As effective passive heat transfer components, heat pipes can be used not only to cool the system but also to insert heat into it. This capability of reversing the heat flow enables a high controllability of the temperature field in a bidirectional way and allows innovative capabilities of using advanced control algorithms. This paper describes the overall heat pipe concept and focuses on its potential as a key element for dynamic temperature control systems. Experimental results prove the feasibility of the concept with a simple on-off controller, achieving the reduction of the TCP displacement variation of a 2.2 kW spindle by 62% of its original value. The potential of the tempering concept forms the base for the deployment of various advanced control systems, such as Model-based Predictive Control (MPC), Fuzzy or Reinforcement Learning.

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Improved thermal resistance network model of motorized spindle system considering temperature variation of cooling system

Cited by 25 publications

References 15 publications

Thermal network model and experimental validation for a motorized spindle including thermal–mechanical coupling effect

Thermal network model and experimental validation for a motorized spindle including thermal–mechanical coupling effect

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

Improving the Thermal Behavior of High-Speed Spindles Through the Use of an Active Controlled Heat Pipe System

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