Analysis of thermal errors in a high-speed micro-milling spindle

Creighton, E. E. F.; Honegger, Andrew E.; Tulsian, A.; Mukhopadhyay, Debashis

doi:10.1016/j.ijmachtools.2009.11.002

Cited by 204 publications

(86 citation statements)

References 12 publications

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“…7 relate to measurements with the spindle stationary for a prolonged period (10 minutes) as opposed to 3 seconds during which measurement was made. Although not shown, z-axis length variation when operating at 30, 40, 50 & 60k rpm was measured and produced greater variation as speed was increased in line with results reported by Bissacco et al [15] and Creighton et al [16].…”

Section: Advances In Precision Engineeringsupporting

confidence: 88%

Measurement of Spindle Thermal Growth on a Machine Intended for Micro/Meso Scale Milling

Saedon

Soo

Aspinwall

2010

KEM

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Abstract. Micro milling is gaining ground as the preferred process for the manufacture of micro/meso-scale components in conventional workpiece materials, in particular for miniature moulds and tooling inserts (~ 60HRC), for the plastics injection moulding industry. Following a brief literature review on microscale milling and associated machine tool/tooling developments, experimental results are presented in relation to spindle thermal growth for a compensated/cooled spindle operating at up to 60,000 rpm, designed to accommodate the machining of mesoscale/micro-scale components. The work involved investigation of spindle warm up and cool down rates for speeds ranging from 30,000 -60,000 rpm and subsequently the evaluation of spindle growth using both non-contact and contact measuring systems. Growth levels of up to 16µm were detected despite active spindle cooling and the incorporation of a standard compensation algorithm within the control system. Modification to spindle acceleration and deceleration rates reduced error levels by up to 50%.

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Section: Advances In Precision Engineeringsupporting

confidence: 88%

Measurement of Spindle Thermal Growth on a Machine Intended for Micro/Meso Scale Milling

Saedon

Soo

Aspinwall

2010

KEM

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“…Specifically, Ruspini Rear bearing cooling out T 4 Front bearing cooling out T 5 Ambient temperature T 6 Front bearing oriented X2 T 7 Front bearing oriented Y + T 8 Motor oriented Y + T 9 The cooling in T 10 Motor cooling out T 11 Motor oriented X2 S 1 Radial near X-axis S 2 Radial near Y-axis S 3 Radial distal X-axis S 4 Radial distal Y-axis S 5 Z axial direction introduced fuzzy set theory into cluster analysis. Other researchers then presented a variety of the fuzzy clustering analysis method based on fuzzy graph theory, including the biggest tree method based on fuzzy graph theory.…”

Section: Temperature Fuzzy Cluster Grouping and Optimisationmentioning

confidence: 99%

Thermal error compensation on a computer numerical control machine tool considering thermal tilt angles and cutting tool length

Yang

Mei

Zhao

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part B:

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The present error compensation technology of computer numerical control machine tools ignores radial thermal tilt angle errors of the spindle, while the thermal-induced offset is closely related to the tilt angle and the handle length. To solve this problem, three models of spindle thermal errors are proposed for the thermal yaw, pitch angles and elongation, and error compensation is performed based on the thermal tilt angles and cutting tool length. A fivepoint method was applied to measure the spindle thermal drifts at different speeds by eddy current sensors, which could effectively analyse the changes in the position-pose of the errors. Fuzzy clustering and correlation analysis were applied to group and optimise the temperature variables and select the variables sensitive to thermal errors in order to depress the multicollinearity of the temperature variables and improve the stability of the model. Finally, the thermal offset compensation was conducted in three directions. The results indicate that back propagation has a better capability for nonlinear fitting, but its generalisation is far less than that of time series. While the structure of multiple linear regression analysis is simple, its prediction accuracy is not satisfied. Time series adequately reflects the dynamic behaviours of the thermal error, and the prediction accuracy can reach 94%, with excellent robustness under different cutting conditions. The thermal error compensation equation that includes thermal tilt angles and cutting tool length is suitable for actual conditions and can accurately describe the space-pose of the thermal deformation and improve the machining accuracy.

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“…Some results have been obtained [15], but the nonlinear of temperature data is not considered adequately. Creighton et al established the model of temperature rise and thermal deformation by using finite element method [16].The temperature rise and thermal deformation of the key points were obtained by using thermocouple and capacitance meter. The optimized process is relatively straightforward, but the boundary conditions of finite element method are not uniform.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Prediction Method of Thermal Extension Error for Boring Machine Based on PCA and LS-SVM

Cheng

et al. 2017

MATEC Web Conf.

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Thermal extension error of boring bar in z-axis is one of the key factors that have a bad influence on the machining accuracy of boring machine, so how to exactly establish the relationship between the thermal extension length and temperature and predict the changing rule of thermal error are the premise of thermal extension error compensation. In this paper, a prediction method of thermal extension length of boring bar in boring machine is proposed based on principal component analysis (PCA) and least squares support vector machine (LS-SVM) model. In order to avoid the multiple correlation and coupling among the great amount temperature input variables, firstly, PCA is introduced to extract the principal components of temperature data samples. Then, LS-SVM is used to predict the changing tendency of the thermally induced thermal extension error of boring bar. Finally, experiments are conducted on a boring machine, the application results show that Boring bar axial thermal elongation error residual value dropped below 5 μm and minimum residual error is only 0.5 μm. This method not only effectively improve the efficiency of the temperature data acquisition and analysis, and improve the modeling accuracy and robustness.

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Analysis of thermal errors in a high-speed micro-milling spindle

Cited by 204 publications

References 12 publications

Measurement of Spindle Thermal Growth on a Machine Intended for Micro/Meso Scale Milling

Measurement of Spindle Thermal Growth on a Machine Intended for Micro/Meso Scale Milling

Thermal error compensation on a computer numerical control machine tool considering thermal tilt angles and cutting tool length

A Hybrid Prediction Method of Thermal Extension Error for Boring Machine Based on PCA and LS-SVM

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