Thermal Error Modeling of a Machining Center Using Grey System Theory and Adaptive Network-Based Fuzzy Inference System

Wang, Kun‐Chieh

doi:10.1299/jsmec.49.1179

Cited by 20 publications

(12 citation statements)

References 20 publications

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“…The displacement of the TCP can be calculated by a regression equation [4][5][6][7][8][9], a neural network [10][11][12][13][14] or a fuzzy logic [15][16][17][18] depending on the machine tool temperature. This compensation method has two main weak points.…”

Section: State Of the Artmentioning

confidence: 99%

Compensation of thermo-dependent machine tool deformations due to spindle load: investigation of the optimal transfer function in consideration of rough machining

Brecher

Wissmann

2011

Prod. Eng. Res. Devel.

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The thermal behavior of a machine tool is an important indicator for the grade of production accuracy and indirectly for the market success. The load-dependent temperature distribution and the resulting deformation of the machine tool are influenced by a variety of design and thermo-technical parameters. The main spindle of a machine tool is, without any doubt, the major heat source within the machine structure. The object of the scientific investigation presented in this article is the development of an approach to robust compensation of thermo-dependent machine tool deformations due to spindle load in consideration of rough machining. The focus of the work concentrates on the identification of the model with the highest compensation performance. The underlying concept for the compensation of thermo-dependent machine tool deformations is the indirect approach by using the speed and the effective power of the main spindle for the calculation of the Tool Center Point (TCP) displacement. The presented modeling approach requires the knowledge of the TCP displacement in X-, Y-and Z-direction depending on the speed and the effective power of the main spindle. As a tool for modeling the thermo-dependent behavior of a milling machine, a load test rig for repeatable, defined long-term loading of the main spindle has been developed. It simulates the cutting force depending on the spindle speed and the torque and applies load to the main spindle. The spindle speed and the spindle effective power can be taken directly from the numerical control of the machine tool. An important advantage of the presented compensation method is the fact that it does not require any external sensors. The displacement of the TCP has to be measured, but only during modeling. The relationship between the speed/power of the main spindle as a cause and the displacement of the TCP in X-, Y-and Z-direction as an effect can be determined by a transfer function. This paper compares the compensation results depending on the transfer function and identifies the model with the best compensation performance. The validation of the compensation method is executed by using the example of two different speed and power spectra of the main spindle.

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Section: State Of the Artmentioning

confidence: 99%

Compensation of thermo-dependent machine tool deformations due to spindle load: investigation of the optimal transfer function in consideration of rough machining

Brecher

Wissmann

2011

Prod. Eng. Res. Devel.

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“…The artificial neural network (ANN) was used to establish a relationship between temperature and the thermal error of a machine tool. [10][11][12][13][14] Although the ANN model was useful for making generalisations, its physical explanation was weak, and its predictive ability was heavily dependent on typical learning samples. Ramesh et al 15 developed a model using support vector machines that could effectively predict thermal errors.…”

Section: Introductionmentioning

confidence: 99%

Thermal error simulation and compensation in a jig-boring machine equipped with a dual-drive servo feed system

Yang

Zhang

Mei

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part B:

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A jig-boring machine equipped with a dual-drive servo system can operate with high speed and accuracy. However, different friction behaviours and asymmetrical preloads of the double drive structures as well as asynchronous control over the master-slave motors can make the machine produce tremendous heat, causing uneven temperature distributions in the feed system and eventually leading to thermal deformation that reduces the positional accuracy of machines. To investigate the effects of thermal behaviours on machining accuracy, the thermal-structure finite element method was employed to analyse the transient thermal deformation and temperature field of the machine at different feed rates, considering boundary conditions such as the convective heat transfer coefficient and heat generation by motors, bearings, and screws. Additionally, a synchronous acquisition system was developed to measure the thermal behaviours, and the transient changes in temperature and deformation were compared with simulated values. Consequently, a synthetic thermal model was established to make accurate predictions based on the analysis of relationships between thermal error and equilibrium time, coordinate position and screw temperature. Finally, thermal error compensation was performed using a feedback integration method. The experimental data indicate that the finite element method model can accurately predict temperature distributions and thermal errors. Moreover, thermal errors were compensated at 24.1°C and 22.6°C with a feed rate of 18 m/min, and machining accuracy was increased by 73% and 62%, respectively.

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“…For the second problem, there are many modelling methods, such as Regression Analysis, Neural Network [8][9][10], Grey System [11,12], Fuzzy Logic [10,13], Least Squares Support Vector Machine [14,15], etc. These methods have produced a good effect on thermal error compensation, but also need to been further improved in terms of model robustness and the simplicity of utilization.…”

Section: Introductionmentioning

confidence: 99%

Thermal error modelling of NC machine tools based on impulse response model

Zhang

Gao

et al. 2013

2013 IEEE International Symposium on Assembly and Manufacturing (ISAM)

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High accuracy of machine tool is the assurance of top-quality products in machining processes. Among the various errors related to machine tools, thermal errors have a significant effect on machining accuracy and directly determine the machined quality of the finished part with both the surface finish and the geometric shape. So, if we establish thermal error model accurately, the affection of thermal error would be eliminated through compensation. How to get the key temperature measuring points and robustness of the model is the main problem of thermal error modelling using traditional methods. Accordingly, the paper presents a novel method for searching key thermal point by means of infrared imaging system and modelling by system identification based on least square principle. The experiments of thermal error compensation have been conducted on a rotary table of five-axis Milling Center. The results show that the key points are selected simply and effectively and the compensation model has good adaptability, thus its prediction is accurate.

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Thermal Error Modeling of a Machining Center Using Grey System Theory and Adaptive Network-Based Fuzzy Inference System

Cited by 20 publications

References 20 publications

Compensation of thermo-dependent machine tool deformations due to spindle load: investigation of the optimal transfer function in consideration of rough machining

Compensation of thermo-dependent machine tool deformations due to spindle load: investigation of the optimal transfer function in consideration of rough machining

Thermal error simulation and compensation in a jig-boring machine equipped with a dual-drive servo feed system

Thermal error modelling of NC machine tools based on impulse response model

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