Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII

Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is...
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Gespeichert in:

Autor*in:	Ruizhi Li [verfasserIn] Zipeng Wang [verfasserIn] Jihong Yan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	grinding robot process parameters multi-objective optimization grinding quality grinding time

Übergeordnetes Werk:	In: Machines - MDPI AG, 2013, 11(2023), 882, p 882
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:882, p 882

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/machines11090882

Katalog-ID:	DOAJ093364717

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allfields	10.3390/machines11090882 doi (DE-627)DOAJ093364717 (DE-599)DOAJb45b65ba4bb74e7eafef21d917e917bf DE-627 ger DE-627 rakwb eng TJ1-1570 Ruizhi Li verfasserin aut Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications. grinding robot process parameters multi-objective optimization grinding quality grinding time Mechanical engineering and machinery Zipeng Wang verfasserin aut Jihong Yan verfasserin aut In Machines MDPI AG, 2013 11(2023), 882, p 882 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:11 year:2023 number:882, p 882 https://doi.org/10.3390/machines11090882 kostenfrei https://doaj.org/article/b45b65ba4bb74e7eafef21d917e917bf kostenfrei https://www.mdpi.com/2075-1702/11/9/882 kostenfrei https://doaj.org/toc/2075-1702 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 11 2023 882, p 882
spelling	10.3390/machines11090882 doi (DE-627)DOAJ093364717 (DE-599)DOAJb45b65ba4bb74e7eafef21d917e917bf DE-627 ger DE-627 rakwb eng TJ1-1570 Ruizhi Li verfasserin aut Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications. grinding robot process parameters multi-objective optimization grinding quality grinding time Mechanical engineering and machinery Zipeng Wang verfasserin aut Jihong Yan verfasserin aut In Machines MDPI AG, 2013 11(2023), 882, p 882 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:11 year:2023 number:882, p 882 https://doi.org/10.3390/machines11090882 kostenfrei https://doaj.org/article/b45b65ba4bb74e7eafef21d917e917bf kostenfrei https://www.mdpi.com/2075-1702/11/9/882 kostenfrei https://doaj.org/toc/2075-1702 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 11 2023 882, p 882
allfields_unstemmed	10.3390/machines11090882 doi (DE-627)DOAJ093364717 (DE-599)DOAJb45b65ba4bb74e7eafef21d917e917bf DE-627 ger DE-627 rakwb eng TJ1-1570 Ruizhi Li verfasserin aut Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications. grinding robot process parameters multi-objective optimization grinding quality grinding time Mechanical engineering and machinery Zipeng Wang verfasserin aut Jihong Yan verfasserin aut In Machines MDPI AG, 2013 11(2023), 882, p 882 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:11 year:2023 number:882, p 882 https://doi.org/10.3390/machines11090882 kostenfrei https://doaj.org/article/b45b65ba4bb74e7eafef21d917e917bf kostenfrei https://www.mdpi.com/2075-1702/11/9/882 kostenfrei https://doaj.org/toc/2075-1702 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 11 2023 882, p 882
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allfieldsSound	10.3390/machines11090882 doi (DE-627)DOAJ093364717 (DE-599)DOAJb45b65ba4bb74e7eafef21d917e917bf DE-627 ger DE-627 rakwb eng TJ1-1570 Ruizhi Li verfasserin aut Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications. grinding robot process parameters multi-objective optimization grinding quality grinding time Mechanical engineering and machinery Zipeng Wang verfasserin aut Jihong Yan verfasserin aut In Machines MDPI AG, 2013 11(2023), 882, p 882 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:11 year:2023 number:882, p 882 https://doi.org/10.3390/machines11090882 kostenfrei https://doaj.org/article/b45b65ba4bb74e7eafef21d917e917bf kostenfrei https://www.mdpi.com/2075-1702/11/9/882 kostenfrei https://doaj.org/toc/2075-1702 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 11 2023 882, p 882
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source	In Machines 11(2023), 882, p 882 volume:11 year:2023 number:882, p 882
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authorswithroles_txt_mv	Ruizhi Li @@aut@@ Zipeng Wang @@aut@@ Jihong Yan @@aut@@
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callnumber-first	T - Technology
author	Ruizhi Li
spellingShingle	Ruizhi Li misc TJ1-1570 misc grinding robot misc process parameters misc multi-objective optimization misc grinding quality misc grinding time misc Mechanical engineering and machinery Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII
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topic_title	TJ1-1570 Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII grinding robot process parameters multi-objective optimization grinding quality grinding time
topic	misc TJ1-1570 misc grinding robot misc process parameters misc multi-objective optimization misc grinding quality misc grinding time misc Mechanical engineering and machinery
topic_unstemmed	misc TJ1-1570 misc grinding robot misc process parameters misc multi-objective optimization misc grinding quality misc grinding time misc Mechanical engineering and machinery
topic_browse	misc TJ1-1570 misc grinding robot misc process parameters misc multi-objective optimization misc grinding quality misc grinding time misc Mechanical engineering and machinery
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title	Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII
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title_full	Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII
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title_sort	multi-objective optimization of the process parameters of a grinding robot using lstm-mlp-nsgaii
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title_auth	Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII
abstract	Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications.
abstractGer	Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications.
abstract_unstemmed	Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications.
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title_short	Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII
url	https://doi.org/10.3390/machines11090882 https://doaj.org/article/b45b65ba4bb74e7eafef21d917e917bf https://www.mdpi.com/2075-1702/11/9/882 https://doaj.org/toc/2075-1702
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Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII

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