Thermal error modeling of machine tool based on fuzzy c-means cluster analysis

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

doi:10.1109/emeit.2011.6023577

Cited by 3 publications

(1 citation statement)

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“…Yuan and Ni [18] adopted the coefficient of correlation (CC) to group the temperature points, and then, in each group, selected the point most correlative to the thermal error to conduct thermal error modeling. Han et al [19] adopted fuzzy c-means (FCM) cluster analysis to group the temperature points, and then in each group selected the point closest to the center point to conduct thermal error modeling. Yang et al [20] adopted k-harmonic means clustering (KHM) to reduce the number of feature temperature points for the thermal error modeling of machine tools.…”

Section: Introductionmentioning

confidence: 99%

The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning

Chiu

Wang

2021

Machines

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Thermal error is one of the main sources of machining error of machine tools. Being a key component of the machine tool, the spindle will generate a lot of heat in the machining process and thereby result in a thermal error of itself. Real-time measurement of thermal error will interrupt the machining process. Therefore, this paper presents a machine learning model to estimate the thermal error of the spindle from its feature temperature points. The authors adopt random forests and Gaussian process regression to model the thermal error of the spindle and Pearson correlation coefficients to select the feature temperature points. The result shows that random forests collocating with Pearson correlation coefficients is an efficient and accurate method for the thermal error modeling of the spindle. Its accuracy reaches to 90.49% based on only four feature temperature points—two points at the bearings and two points at the inner housing—and the spindle speed. If the accuracy requirement is not very onerous, one can select just the temperature points of the bearings, because the installation of temperature sensors at these positions is acceptable for the spindle or machine tool manufacture, while the other positions may interfere with the cooling pipeline of the spindle.

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Section: Introductionmentioning

confidence: 99%