A hybrid physics-data-driven surface roughness prediction model for ultra-precision machining
Abstract The surface finish quality is critical to the service performance of a machined part, and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials. This study studied the problem of predicting the surface roughness for titanium allo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bai, Long [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© Science China Press 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Science in China - Heidelberg : Springer, 1997, 66(2023), 5 vom: 13. Apr., Seite 1289-1303 |
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| Übergeordnetes Werk: |
volume:66 ; year:2023 ; number:5 ; day:13 ; month:04 ; pages:1289-1303 |
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| DOI / URN: |
10.1007/s11431-022-2358-4 |
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10.1007/s11431-022-2358-4 doi (DE-627)SPR050312529 (SPR)s11431-022-2358-4-e DE-627 ger DE-627 rakwb eng Bai, Long verfasserin aut A hybrid physics-data-driven surface roughness prediction model for ultra-precision machining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract The surface finish quality is critical to the service performance of a machined part, and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials. This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining. Process data and surface roughness measurement results were obtained during end-face machining experiments. A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness. We propose increasing prediction accuracy by using the energy ratio difference (ERD) as a stability feature that can be extracted using fast iterative variational mode decomposition (FI-VMD). The roughness value obtained with an analytic model was also used as an input feature of the prediction model. The prediction accuracy of the proposed approach was depicted to be improved by 8.7% with the two newly introduced roughness predictors. The influence of the tool parameters on the prediction accuracy was investigated, and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model. surface roughness (dpeaa)DE-He213 ultra-precision machining (dpeaa)DE-He213 prediction model (dpeaa)DE-He213 stability feature (dpeaa)DE-He213 Yang, QiZhong aut Cheng, Xin aut Ding, Yue aut Xu, JianFeng aut Enthalten in Science in China Heidelberg : Springer, 1997 66(2023), 5 vom: 13. Apr., Seite 1289-1303 (DE-627)385614756 (DE-600)2142897-9 1862-281X nnns volume:66 year:2023 number:5 day:13 month:04 pages:1289-1303 https://dx.doi.org/10.1007/s11431-022-2358-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 66 2023 5 13 04 1289-1303 |
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10.1007/s11431-022-2358-4 doi (DE-627)SPR050312529 (SPR)s11431-022-2358-4-e DE-627 ger DE-627 rakwb eng Bai, Long verfasserin aut A hybrid physics-data-driven surface roughness prediction model for ultra-precision machining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract The surface finish quality is critical to the service performance of a machined part, and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials. This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining. Process data and surface roughness measurement results were obtained during end-face machining experiments. A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness. We propose increasing prediction accuracy by using the energy ratio difference (ERD) as a stability feature that can be extracted using fast iterative variational mode decomposition (FI-VMD). The roughness value obtained with an analytic model was also used as an input feature of the prediction model. The prediction accuracy of the proposed approach was depicted to be improved by 8.7% with the two newly introduced roughness predictors. The influence of the tool parameters on the prediction accuracy was investigated, and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model. surface roughness (dpeaa)DE-He213 ultra-precision machining (dpeaa)DE-He213 prediction model (dpeaa)DE-He213 stability feature (dpeaa)DE-He213 Yang, QiZhong aut Cheng, Xin aut Ding, Yue aut Xu, JianFeng aut Enthalten in Science in China Heidelberg : Springer, 1997 66(2023), 5 vom: 13. Apr., Seite 1289-1303 (DE-627)385614756 (DE-600)2142897-9 1862-281X nnns volume:66 year:2023 number:5 day:13 month:04 pages:1289-1303 https://dx.doi.org/10.1007/s11431-022-2358-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 66 2023 5 13 04 1289-1303 |
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10.1007/s11431-022-2358-4 doi (DE-627)SPR050312529 (SPR)s11431-022-2358-4-e DE-627 ger DE-627 rakwb eng Bai, Long verfasserin aut A hybrid physics-data-driven surface roughness prediction model for ultra-precision machining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract The surface finish quality is critical to the service performance of a machined part, and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials. This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining. Process data and surface roughness measurement results were obtained during end-face machining experiments. A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness. We propose increasing prediction accuracy by using the energy ratio difference (ERD) as a stability feature that can be extracted using fast iterative variational mode decomposition (FI-VMD). The roughness value obtained with an analytic model was also used as an input feature of the prediction model. The prediction accuracy of the proposed approach was depicted to be improved by 8.7% with the two newly introduced roughness predictors. The influence of the tool parameters on the prediction accuracy was investigated, and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model. surface roughness (dpeaa)DE-He213 ultra-precision machining (dpeaa)DE-He213 prediction model (dpeaa)DE-He213 stability feature (dpeaa)DE-He213 Yang, QiZhong aut Cheng, Xin aut Ding, Yue aut Xu, JianFeng aut Enthalten in Science in China Heidelberg : Springer, 1997 66(2023), 5 vom: 13. Apr., Seite 1289-1303 (DE-627)385614756 (DE-600)2142897-9 1862-281X nnns volume:66 year:2023 number:5 day:13 month:04 pages:1289-1303 https://dx.doi.org/10.1007/s11431-022-2358-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 66 2023 5 13 04 1289-1303 |
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10.1007/s11431-022-2358-4 doi (DE-627)SPR050312529 (SPR)s11431-022-2358-4-e DE-627 ger DE-627 rakwb eng Bai, Long verfasserin aut A hybrid physics-data-driven surface roughness prediction model for ultra-precision machining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract The surface finish quality is critical to the service performance of a machined part, and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials. This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining. Process data and surface roughness measurement results were obtained during end-face machining experiments. A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness. We propose increasing prediction accuracy by using the energy ratio difference (ERD) as a stability feature that can be extracted using fast iterative variational mode decomposition (FI-VMD). The roughness value obtained with an analytic model was also used as an input feature of the prediction model. The prediction accuracy of the proposed approach was depicted to be improved by 8.7% with the two newly introduced roughness predictors. The influence of the tool parameters on the prediction accuracy was investigated, and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model. surface roughness (dpeaa)DE-He213 ultra-precision machining (dpeaa)DE-He213 prediction model (dpeaa)DE-He213 stability feature (dpeaa)DE-He213 Yang, QiZhong aut Cheng, Xin aut Ding, Yue aut Xu, JianFeng aut Enthalten in Science in China Heidelberg : Springer, 1997 66(2023), 5 vom: 13. Apr., Seite 1289-1303 (DE-627)385614756 (DE-600)2142897-9 1862-281X nnns volume:66 year:2023 number:5 day:13 month:04 pages:1289-1303 https://dx.doi.org/10.1007/s11431-022-2358-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 66 2023 5 13 04 1289-1303 |
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10.1007/s11431-022-2358-4 doi (DE-627)SPR050312529 (SPR)s11431-022-2358-4-e DE-627 ger DE-627 rakwb eng Bai, Long verfasserin aut A hybrid physics-data-driven surface roughness prediction model for ultra-precision machining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract The surface finish quality is critical to the service performance of a machined part, and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials. This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining. Process data and surface roughness measurement results were obtained during end-face machining experiments. A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness. We propose increasing prediction accuracy by using the energy ratio difference (ERD) as a stability feature that can be extracted using fast iterative variational mode decomposition (FI-VMD). The roughness value obtained with an analytic model was also used as an input feature of the prediction model. The prediction accuracy of the proposed approach was depicted to be improved by 8.7% with the two newly introduced roughness predictors. The influence of the tool parameters on the prediction accuracy was investigated, and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model. surface roughness (dpeaa)DE-He213 ultra-precision machining (dpeaa)DE-He213 prediction model (dpeaa)DE-He213 stability feature (dpeaa)DE-He213 Yang, QiZhong aut Cheng, Xin aut Ding, Yue aut Xu, JianFeng aut Enthalten in Science in China Heidelberg : Springer, 1997 66(2023), 5 vom: 13. Apr., Seite 1289-1303 (DE-627)385614756 (DE-600)2142897-9 1862-281X nnns volume:66 year:2023 number:5 day:13 month:04 pages:1289-1303 https://dx.doi.org/10.1007/s11431-022-2358-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 66 2023 5 13 04 1289-1303 |
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A hybrid physics-data-driven surface roughness prediction model for ultra-precision machining |
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Abstract The surface finish quality is critical to the service performance of a machined part, and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials. This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining. Process data and surface roughness measurement results were obtained during end-face machining experiments. A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness. We propose increasing prediction accuracy by using the energy ratio difference (ERD) as a stability feature that can be extracted using fast iterative variational mode decomposition (FI-VMD). The roughness value obtained with an analytic model was also used as an input feature of the prediction model. The prediction accuracy of the proposed approach was depicted to be improved by 8.7% with the two newly introduced roughness predictors. The influence of the tool parameters on the prediction accuracy was investigated, and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model. © Science China Press 2023 |
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Abstract The surface finish quality is critical to the service performance of a machined part, and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials. This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining. Process data and surface roughness measurement results were obtained during end-face machining experiments. A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness. We propose increasing prediction accuracy by using the energy ratio difference (ERD) as a stability feature that can be extracted using fast iterative variational mode decomposition (FI-VMD). The roughness value obtained with an analytic model was also used as an input feature of the prediction model. The prediction accuracy of the proposed approach was depicted to be improved by 8.7% with the two newly introduced roughness predictors. The influence of the tool parameters on the prediction accuracy was investigated, and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model. © Science China Press 2023 |
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Abstract The surface finish quality is critical to the service performance of a machined part, and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials. This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining. Process data and surface roughness measurement results were obtained during end-face machining experiments. A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness. We propose increasing prediction accuracy by using the energy ratio difference (ERD) as a stability feature that can be extracted using fast iterative variational mode decomposition (FI-VMD). The roughness value obtained with an analytic model was also used as an input feature of the prediction model. The prediction accuracy of the proposed approach was depicted to be improved by 8.7% with the two newly introduced roughness predictors. The influence of the tool parameters on the prediction accuracy was investigated, and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model. © Science China Press 2023 |
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