Thermal modelling of an HSC machining centre to predict thermal error of the feed system

Uhlmann, Eckart; Hu, Jiangmin

doi:10.1007/s11740-012-0406-6

Cited by 18 publications

(6 citation statements)

References 7 publications

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“…Zhang et al [8] performed a weakness analysis on FEM thermal behavior results of the spindle head of a vertical machine tool, leading to structural redesign and optimization of design parameters during the prototype phase. Uhlmann et al [9] employed FEM to establish a thermal model for vertical machine tools to predict thermal deformation, refining accuracy through comparison with experimental values. Tu et al [10] developed a Finite-Difference Methods (FDM) thermal model to represent the power distribution of a high-speed spindle, mainly focusing on heat transfer and radiator performance.…”

Section: Introductionmentioning

confidence: 99%

Study on the Thermal Characteristics of a Turning and Milling Machine Using Taguchi-FEM with ANOVA-Driven Search

Wu,

Hsieh,

Kuo

et al. 2024

Preprint

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Turning and milling machines (TMMs) have diverse and complex heat sources, which pose challenges to understanding and accurately predicting their thermal behavior. This study established a transient 3D thermal model of the TMM using the Finite Element Method (FEM) to predict the temporal evolution of temperature and deformation fields. The FEM predictions were then compared with the experimental results from five machining scenarios for model validation, demonstrating accurate prediction of temperature and deformation with average errors of 0.9% and 5.59 μm, respectively. A comprehensive procedure was proposed to explore the thermal characteristics of TMMs and identify thermally critical components. This study employed a Taguchi-FEM approach along with sensitivity analysis to evaluate the significant contributing boundary conditions of TMMs to tool center point (TCP) deformation in the X, Y, and Z directions. An ANOVA-driven search was further proposed to identify thermal key points significantly impacting TCP deformation across directions and time intervals. Integrating both methods facilitated the identification of the TMM thermal critical regions. The spindle TCP’s thermal critical regions were most significantly influenced by the spindle component, followed by the milling cutter shaft component and then the right side of the base bed. Similarly, for the sub-spindle TCP, the highest impact came from the sub-spindle components, followed by the right side of the base bed and the milling cutter shaft components. The proposed procedure identified the significant regions for assisting evaluation in TMMs and reduced the number of required temperature monitoring points while optimizing their placements to minimize measurement costs.

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Section: Introductionmentioning

confidence: 99%

Study on the Thermal Characteristics of a Turning and Milling Machine Using Taguchi-FEM with ANOVA-Driven Search

Wu,

Hsieh,

Kuo

et al. 2024

Preprint

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“…But only the boundary conditions of the spindle system were considered, and the other system was ignored. Uhlmann et al [11] proposed a thermal model to simulate the thermal behavior of a high-speed machining center equipped with a linear motor by using the finite element method. The complex boundary conditions such as heat source, contact and convective heat transfer were considered in this model.…”

Section: Introductionmentioning

confidence: 99%

Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

et al. 2021

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The linear motor feed system has been in service in complex working conditions for a long time, thus causing the nonuniform distribution of the temperature field distribution. Thus, thermal error has become a key factor affecting system motion accuracy. To maximize the accuracy and efficiency of thermal error compensation for linear motor feed system, an improved modeling method for the thermal error of the linear motor feed system based on Bayesian neural networks is proposed in combination with the strong generalization performance and avoidance of overfitting of Bayesian neural networks. And the specific modeling ideas are as follows: Firstly, the X-Y cross-type two-axis linear motor feed system is as the test object. Aiming at the slow convergence , over fitting and under fitting problems of traditional neural network, Bayesian neural network is used to model the thermal error of linear motor feed system .Secondly, In order to avoid the influence of multicollinearity data on the final results, the grey relation analysis method is used to screen the temperature measuring points, and the data with large relation degree is selected for modeling to ensure the prediction accuracy of neural network. Thirdly, and the temperature variables of sensitive points and thermal positioning errors are taken as data input samples. Fourthly, a Bayesian neural network model is established. Fifthly, the hyperparameters of the Bayesian neural network is determined by a calculating method of Hessian matrix by Gauss Newton approximation. And finally, a thermal error prediction model is established. The comparison and analysis with the neural network constructed by ordinary Levenberg-Marquardt algorithm after a series of experimental demonstrations see that the prediction accuracy of the proposed method can be enhanced by up to 10%. It also shows that the prediction model has the advantages of high precision, strong generalization ability, strong anti-disturbance ability and strong robustness, etc. Therefore, the prediction model is expected to be widely used in the prediction and compensation of thermal error of the feed system of high-speed CNC machine tools in practical machining occasions.

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“…have been widely used to analyze the structural temperature field [15,16,17,18,19,20]. Uhlmann [21] proposed a new thermodynamic model, which used finite element simulation to calculate the thermodynamic behavior of the high-speed cutting center. Zheng [22] established an optimized thermal resistance network model to estimate the temperature field of the bearing system.…”

Section: Introductionmentioning

confidence: 99%

Heat Source Forecast of Ball Screw Drive System Under Actual Working Conditions Based on On-Line Measurement of Temperature Sensors

Zhong-wu

Zhao

et al. 2019

Sensors

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In view of the time-varying complexity of the heat source for the ball screw feed system, this paper proposes an adaptive inverse problem-solving method to estimate the time-varying heat source and temperature field of the feed system under working conditions. The feed system includes multiple heat sources, and the rapid change of the moving heat source increases the difficulty of its identification. This paper attempts to develop a numerical calculation method for identifying the heat source by combining the experiment with the optimization algorithm. Firstly, based on the theory of heat transfer, a new dynamic thermal network model was proposed. The temperature data signal and the position signal of the moving nut captured by the sensors are used as input to optimize the solution of the time-varying heat source. Then, based on the data obtained from the experiment, finite element software parametric programming was used to optimize the estimate of the heat source, and the results of the two heat source prediction methods are compared and verified. The other measured temperature points obtained by the experiment were used to compare and verify the inverse method of this numerical calculation, which illustrates the reliability and advantages of the dynamic thermal network combined with the genetic algorithm for the inverse method. The method based on the on-line monitoring of temperature sensors proposed in this paper has a strong application value for heat source and temperature field estimation of complex mechanical structures.

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Thermal modelling of an HSC machining centre to predict thermal error of the feed system

Cited by 18 publications

References 7 publications

Study on the Thermal Characteristics of a Turning and Milling Machine Using Taguchi-FEM with ANOVA-Driven Search

Study on the Thermal Characteristics of a Turning and Milling Machine Using Taguchi-FEM with ANOVA-Driven Search

Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

Heat Source Forecast of Ball Screw Drive System Under Actual Working Conditions Based on On-Line Measurement of Temperature Sensors

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