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

Han, Jian; Wang, Liping; Cheng, Ningbo; Wang, Haitong

doi:10.1007/s00170-011-3619-5

Cited by 34 publications

(12 citation statements)

References 25 publications

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“…The relationship between the input u(k À 1) and output y(k) is established by function (5) to (8). So if we know the input u(k À 1), then after the training by OHF-Elman neural network, we get the output y(k).…”

Section: Ohf-elman Neural Networkmentioning

confidence: 99%

Novel thermal error modeling method for machining centers

Zhu

Xiang

Yang

2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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Thermal deformation is one of the main contributors to machining errors in machine tools. In this paper, a novel approach to build an effective thermal error model for a machining center is proposed. Adaptive vector quantization network clustering algorithm is conducted to identify the temperature variables, and then one temperature variable is selected from each cluster to represent the same cluster. Furthermore, a non-linear model based on output-hidden feedback Elman neural network is adopted to establish the relationship between thermal error and temperature variables. The output-hidden feedback Elman network is adopted to predict the thermal deformation of the machining center. Back propagation (BP) neural network is introduced for comparison. A verification experiment on the machining center is carried out to validate the efficiency of the newly proposed method. Experimental verification shows that the adaptive vector quantization network clustering algorithm and output-hidden feedback Elman neural network is an accurate and effective method.

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Section: Ohf-elman Neural Networkmentioning

confidence: 99%

Novel thermal error modeling method for machining centers

Zhu

Xiang

Yang

2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Two key items exist in error prediction, selection of temperature sensor and the suitability of the thermal error model for different machine conditions [3]. In the past few years error compensation technology has received wide attention [4]. In 1996 Chen [5] used computer numerical control milling machine as the experiment platform to build four thermal error models, low-speed rotation (1000 rpm) pneumatic cutting, high-speed rotation (5000 rpm) pneumatic cutting, precision processing cutting, and rough cut aluminum material.…”

Section: Introductionmentioning

confidence: 99%

Thermal monitoring and thermal deformation prediction for spherical machine tool spindles

Tian

Yau

et al. 2019

THERM SCI

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Machine tool operations and processing can cause temperature changes in various components because of internal and external thermal effects. Thermal deformations caused by thermal effect in machine tools can result in errors in processing size or shape and decrease processing precision. Thus, this paper focuses on the analysis of heating during machine tool spindle's high speed operation, which is the heat source that causes component and structural deformation. In this paper, thermal monitoring was used to build a thermal error prediction model. Temperature change around the spindle was measured with a DS18B20, then multiple regression analysis was used to establish the relationship between thermal deformation quantity and temperature fields at specific points. Finally, finite element analysis was used to build the thermal error model. A solution for the correlation coefficient was obtained using the least squares method. The result of this study verified that finite element analysis can predict front bearing and rear bearing temperature rise, and is consistent with laboratory results. The error in thermal steady-state deformation prediction was less than 2 μm. This information can be used by the controller to effectively compensate the processing and improve processing precision.

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“…The development of the modern machine tools proceeds towards high precision and speed, and highlights the importance of thermal effects on mechanical systems. The thermal error accounts for almost 40-70 % of the error sources in a machine tool (Han et al, 2012) The spindle-bearing system is a core component of the machine tool. The performance of the spindle-bearing system is of great significance to the machine tool.…”

Section: Introductionmentioning

confidence: 99%

A dynamic thermal-mechanical model of the spindle-bearing system

2017

Self Cite

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Abstract. This paper presents a dynamic thermal-mechanical model to investigate the thermal characteristics in a spindle-bearing system. In this model, transient thermal analysis, static structure analysis and calculation of the boundary conditions are conducted as a solution loop. The transient boundary conditions, such as bearing stiffness, bearing heat generation and thermal contact conductance, are calculated with the appropriate formulas and solution methodology. The thermal feedbacks, which are seldom considered in the previous studies, are calculated in details. In order to validate the prediction accuracy, thermal equilibrium experiment is conducted on a test rig of spindle-bearing system. The predictions of the proposed model, such as bearing preload, temperature and thermal displacements, are in close agreement with the experiment results. The comparisons of the proposed model with two traditional simulations show that, the thermal feedbacks on the boundary conditions and thermal contact conductance are of great importance to the real-time estimation of the thermal characteristics. The proposed model provides a practical method to improve the prediction accuracy. It could also be generalized in other mechanical systems to investigate the dynamic thermal characteristics.

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

Cited by 34 publications

References 25 publications

Novel thermal error modeling method for machining centers

Novel thermal error modeling method for machining centers

Thermal monitoring and thermal deformation prediction for spherical machine tool spindles

A dynamic thermal-mechanical model of the spindle-bearing system

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