Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network—support vector machine model

Ramesh, R.; Mannan, MA; Poo, A.N.; Keerthi, S.S.

doi:10.1016/s0890-6955(02)00264-x

Cited by 108 publications

(32 citation statements)

References 12 publications

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“…But one should bear in mind that the machining process introduces many disturbances which until now have not been taken into account in thermal error compensation. [170] Artificial neural networks (ANN) have been used to model thermal errors in positioning for axes with preloaded ball screws [171,172]. A new generation artificial neural network based on the wavelet theory deserves attention [113].…”

Section: Modelling and Computing Thermal Errors Generated In Linear Axesmentioning

confidence: 99%

Thermal issues in machine tools

et al. 2012

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Section: Modelling and Computing Thermal Errors Generated In Linear Axesmentioning

confidence: 99%

Thermal issues in machine tools

et al. 2012

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“…South Korean scholar Lee et al established thermal error model of horizontal machining center based on fuzzy logic strategy [34]. Singapore Ramesh et al established thermal error model, which is bayesian networks supporting vector machine (SVM) [35]. American scholars Yang and Ni did thermal elastic analysis of machine tool system in view of dynamic characteristics.…”

Section: Research Progress Of Thermal Deformation Control and High Efmentioning

confidence: 99%

The Research Status and Progress of Heavy/Large Hydrostatic Thrust Bearing

Wang

et al. 2014

Advances in Mechanical Engineering

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How to improve the rotation speed of heavy/large CNC vertical lathe, the machining efficiency, and machining precision is one of the key issues which need to be solved urgently. Hydrostatic thrust bearing is the key part to the heavy/large CNC vertical lathe; its performance directly affects the machining quality and operation efficiency. This paper analyses the latest research results from the perspective of the mechanical properties of hydrostatic thrust bearing, oil film lubrication, static pressure bearing thermal deformation, and the high efficiency refrigeration and evaluates the future scientific research direction in this area. Analysis shows that with the development of hydrostatic thrust bearing to the high speed, high precision, high efficiency, high stability, high multifunction, and high power, the study of hydrostatic thrust bearing will focus on the optimal design of the oil chamber to produce the least amount of heat, how to control the thermal deformation of hydrostatic thrust bearing, and the high efficiency refrigeration to ensure the machining accuracy of CNC equipment.

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“…Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs or to find patterns in data [16,17]. A neural network is able to work parallel with input variables and consequently handle large sets of data swiftly [24].…”

Section: Principle Of the Fuzzy C-means Clusteringmentioning

confidence: 99%

“…Yang [11,12] presented a thermal error model using two mathematic schemes: GM(1, N) model of the grey system theory and the adaptive network-based fuzzy inference system. Different types of neural network have been employed in error modeling, such as cerebella model articulation controller neural network [13], independent component analysis [14], fuzzy artificial resonance theory (fuzzy ART-map) [15], radial basis function (RBF) neural networks [16], hybrid Bayesian network (SVM) model [17].…”

Section: Introductionmentioning

confidence: 99%

Thermal error modeling of machine tool based on fuzzy c-means cluster analysis and minimal-resource allocating networks

Han

Wang

Cheng

et al. 2011

Int J Adv Manuf Technol

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Thermal deformation in machine tools is one of the most significant causes of machining errors. A new approach to predict the thermal error of machine tool is proposed. The temperature variables and the thermal errors are measured using the Pt-100 thermal resistances and eddy current sensors respectively. Fuzzy c-means clustering method is conducted to identify the temperatures, and the representative as an independent variable are selected meanwhile it eliminates the coupling among the variables. The learning and prediction of the thermal errors is achieved using minimal-resource allocating networks by treating the issue as functional mapping between the thermal shifts and the temperature variables. The network is made to predict the error map of a machining center. A traditional radial basis function model is introduced for comparison. The experiment result shows that the fuzzy cmeans clustering method and minimal-resource allocating networks combination is a fast and accurate method for thermal error compensation in machine tools.

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Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network—support vector machine model

Cited by 108 publications

References 12 publications

Thermal issues in machine tools

Thermal issues in machine tools

The Research Status and Progress of Heavy/Large Hydrostatic Thrust Bearing

Thermal error modeling of machine tool based on fuzzy c-means cluster analysis and minimal-resource allocating networks

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