Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control

During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based o...
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Gespeichert in:

Autor*in:	Lilan Liu [verfasserIn] Zifeng Xu [verfasserIn] Chaojia Gao [verfasserIn] Tingting Zhang [verfasserIn] Zenggui Gao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	digital twin gray wolf optimized variational modal decomposition long short-term memory networks torque prediction online control

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 19, p 7282
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:19, p 7282

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22197282

Katalog-ID:	DOAJ086328972

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520			\|a During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line.
650		4	\|a digital twin
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allfields	10.3390/s22197282 doi (DE-627)DOAJ086328972 (DE-599)DOAJb84d5c578fd445b2ac48bccc1347d379 DE-627 ger DE-627 rakwb eng TP1-1185 Lilan Liu verfasserin aut Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line. digital twin gray wolf optimized variational modal decomposition long short-term memory networks torque prediction online control Chemical technology Zifeng Xu verfasserin aut Chaojia Gao verfasserin aut Tingting Zhang verfasserin aut Zenggui Gao verfasserin aut In Sensors MDPI AG, 2003 22(2022), 19, p 7282 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:19, p 7282 https://doi.org/10.3390/s22197282 kostenfrei https://doaj.org/article/b84d5c578fd445b2ac48bccc1347d379 kostenfrei https://www.mdpi.com/1424-8220/22/19/7282 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 19, p 7282
spelling	10.3390/s22197282 doi (DE-627)DOAJ086328972 (DE-599)DOAJb84d5c578fd445b2ac48bccc1347d379 DE-627 ger DE-627 rakwb eng TP1-1185 Lilan Liu verfasserin aut Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line. digital twin gray wolf optimized variational modal decomposition long short-term memory networks torque prediction online control Chemical technology Zifeng Xu verfasserin aut Chaojia Gao verfasserin aut Tingting Zhang verfasserin aut Zenggui Gao verfasserin aut In Sensors MDPI AG, 2003 22(2022), 19, p 7282 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:19, p 7282 https://doi.org/10.3390/s22197282 kostenfrei https://doaj.org/article/b84d5c578fd445b2ac48bccc1347d379 kostenfrei https://www.mdpi.com/1424-8220/22/19/7282 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 19, p 7282
allfields_unstemmed	10.3390/s22197282 doi (DE-627)DOAJ086328972 (DE-599)DOAJb84d5c578fd445b2ac48bccc1347d379 DE-627 ger DE-627 rakwb eng TP1-1185 Lilan Liu verfasserin aut Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line. digital twin gray wolf optimized variational modal decomposition long short-term memory networks torque prediction online control Chemical technology Zifeng Xu verfasserin aut Chaojia Gao verfasserin aut Tingting Zhang verfasserin aut Zenggui Gao verfasserin aut In Sensors MDPI AG, 2003 22(2022), 19, p 7282 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:19, p 7282 https://doi.org/10.3390/s22197282 kostenfrei https://doaj.org/article/b84d5c578fd445b2ac48bccc1347d379 kostenfrei https://www.mdpi.com/1424-8220/22/19/7282 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 19, p 7282
allfieldsGer	10.3390/s22197282 doi (DE-627)DOAJ086328972 (DE-599)DOAJb84d5c578fd445b2ac48bccc1347d379 DE-627 ger DE-627 rakwb eng TP1-1185 Lilan Liu verfasserin aut Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line. digital twin gray wolf optimized variational modal decomposition long short-term memory networks torque prediction online control Chemical technology Zifeng Xu verfasserin aut Chaojia Gao verfasserin aut Tingting Zhang verfasserin aut Zenggui Gao verfasserin aut In Sensors MDPI AG, 2003 22(2022), 19, p 7282 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:19, p 7282 https://doi.org/10.3390/s22197282 kostenfrei https://doaj.org/article/b84d5c578fd445b2ac48bccc1347d379 kostenfrei https://www.mdpi.com/1424-8220/22/19/7282 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 19, p 7282
allfieldsSound	10.3390/s22197282 doi (DE-627)DOAJ086328972 (DE-599)DOAJb84d5c578fd445b2ac48bccc1347d379 DE-627 ger DE-627 rakwb eng TP1-1185 Lilan Liu verfasserin aut Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line. digital twin gray wolf optimized variational modal decomposition long short-term memory networks torque prediction online control Chemical technology Zifeng Xu verfasserin aut Chaojia Gao verfasserin aut Tingting Zhang verfasserin aut Zenggui Gao verfasserin aut In Sensors MDPI AG, 2003 22(2022), 19, p 7282 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:19, p 7282 https://doi.org/10.3390/s22197282 kostenfrei https://doaj.org/article/b84d5c578fd445b2ac48bccc1347d379 kostenfrei https://www.mdpi.com/1424-8220/22/19/7282 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 19, p 7282
language	English
source	In Sensors 22(2022), 19, p 7282 volume:22 year:2022 number:19, p 7282
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authorswithroles_txt_mv	Lilan Liu @@aut@@ Zifeng Xu @@aut@@ Chaojia Gao @@aut@@ Tingting Zhang @@aut@@ Zenggui Gao @@aut@@
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callnumber-first	T - Technology
author	Lilan Liu
spellingShingle	Lilan Liu misc TP1-1185 misc digital twin misc gray wolf optimized variational modal decomposition misc long short-term memory networks misc torque prediction misc online control misc Chemical technology Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control
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topic_title	TP1-1185 Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control digital twin gray wolf optimized variational modal decomposition long short-term memory networks torque prediction online control
topic	misc TP1-1185 misc digital twin misc gray wolf optimized variational modal decomposition misc long short-term memory networks misc torque prediction misc online control misc Chemical technology
topic_unstemmed	misc TP1-1185 misc digital twin misc gray wolf optimized variational modal decomposition misc long short-term memory networks misc torque prediction misc online control misc Chemical technology
topic_browse	misc TP1-1185 misc digital twin misc gray wolf optimized variational modal decomposition misc long short-term memory networks misc torque prediction misc online control misc Chemical technology
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title	Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control
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title_full	Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control
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author_browse	Lilan Liu Zifeng Xu Chaojia Gao Tingting Zhang Zenggui Gao
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doi_str_mv	10.3390/s22197282
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title_sort	digital twin-driven rear axle assembly torque prediction and online control
callnumber	TP1-1185
title_auth	Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control
abstract	During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line.
abstractGer	During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line.
abstract_unstemmed	During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line.
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container_issue	19, p 7282
title_short	Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control
url	https://doi.org/10.3390/s22197282 https://doaj.org/article/b84d5c578fd445b2ac48bccc1347d379 https://www.mdpi.com/1424-8220/22/19/7282 https://doaj.org/toc/1424-8220
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author2	Zifeng Xu Chaojia Gao Tingting Zhang Zenggui Gao
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doi_str	10.3390/s22197282
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up_date	2024-07-03T20:00:28.462Z
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Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control

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