The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning
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 machini...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/9/9/184 |
| _version_ | 1797518441225125888 |
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| author | Yu-Cheng Chiu Po-Hsun Wang Yuh-Chung Hu |
| author_facet | Yu-Cheng Chiu Po-Hsun Wang Yuh-Chung Hu |
| author_sort | Yu-Cheng Chiu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:29:55Z |
| format | Article |
| id | doaj.art-5a159d861828484694e650750227e007 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T07:29:55Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-5a159d861828484694e650750227e0072023-11-22T13:57:35ZengMDPI AGMachines2075-17022021-08-019918410.3390/machines9090184The Thermal Error Estimation of the Machine Tool Spindle Based on Machine LearningYu-Cheng Chiu0Po-Hsun Wang1Yuh-Chung Hu2Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26041, TaiwanDepartment of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26041, TaiwanDepartment of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26041, TaiwanThermal 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.https://www.mdpi.com/2075-1702/9/9/184Gaussian process regressionmachine learningmachine tool spindlePearson correlation coefficientrandom forestthermal error |
| spellingShingle | Yu-Cheng Chiu Po-Hsun Wang Yuh-Chung Hu The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning Machines Gaussian process regression machine learning machine tool spindle Pearson correlation coefficient random forest thermal error |
| title | The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning |
| title_full | The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning |
| title_fullStr | The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning |
| title_full_unstemmed | The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning |
| title_short | The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning |
| title_sort | thermal error estimation of the machine tool spindle based on machine learning |
| topic | Gaussian process regression machine learning machine tool spindle Pearson correlation coefficient random forest thermal error |
| url | https://www.mdpi.com/2075-1702/9/9/184 |
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