The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure
Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. Th...
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| Autor*in: |
Chen, Chienchang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - London : Springer, 1985, 130(2023), 5-6 vom: 16. Dez., Seite 2423-2442 |
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| Übergeordnetes Werk: |
volume:130 ; year:2023 ; number:5-6 ; day:16 ; month:12 ; pages:2423-2442 |
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| DOI / URN: |
10.1007/s00170-023-12778-z |
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SPR054256429 |
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| 520 | |a Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. | ||
| 650 | 4 | |a Thermal error compensation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CNC machine tool |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Dai, Hongjie |4 aut | |
| 700 | 1 | |a Lee, Chunghong |4 aut | |
| 700 | 1 | |a Hsieh, Tunghsien |4 aut | |
| 700 | 1 | |a Hung, Weicheng |4 aut | |
| 700 | 1 | |a Jywe, Wenyuh |4 aut | |
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10.1007/s00170-023-12778-z doi (DE-627)SPR054256429 (SPR)s00170-023-12778-z-e DE-627 ger DE-627 rakwb eng Chen, Chienchang verfasserin (orcid)0009-0008-0103-3089 aut The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. Thermal error compensation (dpeaa)DE-He213 CNC machine tool (dpeaa)DE-He213 Ridge regression (dpeaa)DE-He213 Dai, Hongjie aut Lee, Chunghong aut Hsieh, Tunghsien aut Hung, Weicheng aut Jywe, Wenyuh aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 130(2023), 5-6 vom: 16. Dez., Seite 2423-2442 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:130 year:2023 number:5-6 day:16 month:12 pages:2423-2442 https://dx.doi.org/10.1007/s00170-023-12778-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2023 5-6 16 12 2423-2442 |
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10.1007/s00170-023-12778-z doi (DE-627)SPR054256429 (SPR)s00170-023-12778-z-e DE-627 ger DE-627 rakwb eng Chen, Chienchang verfasserin (orcid)0009-0008-0103-3089 aut The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. Thermal error compensation (dpeaa)DE-He213 CNC machine tool (dpeaa)DE-He213 Ridge regression (dpeaa)DE-He213 Dai, Hongjie aut Lee, Chunghong aut Hsieh, Tunghsien aut Hung, Weicheng aut Jywe, Wenyuh aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 130(2023), 5-6 vom: 16. Dez., Seite 2423-2442 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:130 year:2023 number:5-6 day:16 month:12 pages:2423-2442 https://dx.doi.org/10.1007/s00170-023-12778-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2023 5-6 16 12 2423-2442 |
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10.1007/s00170-023-12778-z doi (DE-627)SPR054256429 (SPR)s00170-023-12778-z-e DE-627 ger DE-627 rakwb eng Chen, Chienchang verfasserin (orcid)0009-0008-0103-3089 aut The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. Thermal error compensation (dpeaa)DE-He213 CNC machine tool (dpeaa)DE-He213 Ridge regression (dpeaa)DE-He213 Dai, Hongjie aut Lee, Chunghong aut Hsieh, Tunghsien aut Hung, Weicheng aut Jywe, Wenyuh aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 130(2023), 5-6 vom: 16. Dez., Seite 2423-2442 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:130 year:2023 number:5-6 day:16 month:12 pages:2423-2442 https://dx.doi.org/10.1007/s00170-023-12778-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2023 5-6 16 12 2423-2442 |
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10.1007/s00170-023-12778-z doi (DE-627)SPR054256429 (SPR)s00170-023-12778-z-e DE-627 ger DE-627 rakwb eng Chen, Chienchang verfasserin (orcid)0009-0008-0103-3089 aut The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. Thermal error compensation (dpeaa)DE-He213 CNC machine tool (dpeaa)DE-He213 Ridge regression (dpeaa)DE-He213 Dai, Hongjie aut Lee, Chunghong aut Hsieh, Tunghsien aut Hung, Weicheng aut Jywe, Wenyuh aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 130(2023), 5-6 vom: 16. Dez., Seite 2423-2442 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:130 year:2023 number:5-6 day:16 month:12 pages:2423-2442 https://dx.doi.org/10.1007/s00170-023-12778-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2023 5-6 16 12 2423-2442 |
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10.1007/s00170-023-12778-z doi (DE-627)SPR054256429 (SPR)s00170-023-12778-z-e DE-627 ger DE-627 rakwb eng Chen, Chienchang verfasserin (orcid)0009-0008-0103-3089 aut The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. Thermal error compensation (dpeaa)DE-He213 CNC machine tool (dpeaa)DE-He213 Ridge regression (dpeaa)DE-He213 Dai, Hongjie aut Lee, Chunghong aut Hsieh, Tunghsien aut Hung, Weicheng aut Jywe, Wenyuh aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 130(2023), 5-6 vom: 16. Dez., Seite 2423-2442 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:130 year:2023 number:5-6 day:16 month:12 pages:2423-2442 https://dx.doi.org/10.1007/s00170-023-12778-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 130 2023 5-6 16 12 2423-2442 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. 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Chen, Chienchang |
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Chen, Chienchang misc Thermal error compensation misc CNC machine tool misc Ridge regression The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure |
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The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure Thermal error compensation (dpeaa)DE-He213 CNC machine tool (dpeaa)DE-He213 Ridge regression (dpeaa)DE-He213 |
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development of thermal error compensation on cnc machine tools by combining ridge parameter selection and backward elimination procedure |
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The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure |
| abstract |
Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the “R–DR model.” The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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The development of thermal error compensation on CNC machine tools by combining ridge parameter selection and backward elimination procedure |
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Dai, Hongjie Lee, Chunghong Hsieh, Tunghsien Hung, Weicheng Jywe, Wenyuh |
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10.1007/s00170-023-12778-z |
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2024-07-04T00:43:14.197Z |
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| score |
7.401267 |