Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II

Abstract To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algor...
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Autor*in:	He, Xiao [verfasserIn] Li, Taifu [verfasserIn] Li, Qiaoyue [verfasserIn] Yang, Jie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Abrasive belt polishing machine Crankshaft connecting rod journal Process parameter optimization BP neural network NSGA-II

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. 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.

Übergeordnetes Werk:	Enthalten in: The international journal of advanced manufacturing technology - Springer London, 1985, 134(2024), 7-8 vom: 09. Sept., Seite 3971-3983
Übergeordnetes Werk:	volume:134 ; year:2024 ; number:7-8 ; day:09 ; month:09 ; pages:3971-3983

Links:	Volltext

DOI / URN:	10.1007/s00170-024-14250-y

Katalog-ID:	SPR057299722

Internformat


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520			\|a Abstract To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time.
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author_variant	x h xh t l tl q l ql j y jy
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allfields	10.1007/s00170-024-14250-y doi (DE-627)SPR057299722 (SPR)s00170-024-14250-y-e DE-627 ger DE-627 rakwb eng 670 VZ 670 VZ 52.70 bkl 52.74 bkl He, Xiao verfasserin aut Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II 2024 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 2024. 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 To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time. Abrasive belt polishing machine (dpeaa)DE-He213 Crankshaft connecting rod journal (dpeaa)DE-He213 Process parameter optimization (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Li, Taifu verfasserin aut Li, Qiaoyue verfasserin aut Yang, Jie verfasserin aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 134(2024), 7-8 vom: 09. Sept., Seite 3971-3983 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:134 year:2024 number:7-8 day:09 month:09 pages:3971-3983 https://dx.doi.org/10.1007/s00170-024-14250-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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 52.70 VZ 52.74 VZ AR 134 2024 7-8 09 09 3971-3983
spelling	10.1007/s00170-024-14250-y doi (DE-627)SPR057299722 (SPR)s00170-024-14250-y-e DE-627 ger DE-627 rakwb eng 670 VZ 670 VZ 52.70 bkl 52.74 bkl He, Xiao verfasserin aut Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II 2024 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 2024. 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 To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time. Abrasive belt polishing machine (dpeaa)DE-He213 Crankshaft connecting rod journal (dpeaa)DE-He213 Process parameter optimization (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Li, Taifu verfasserin aut Li, Qiaoyue verfasserin aut Yang, Jie verfasserin aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 134(2024), 7-8 vom: 09. Sept., Seite 3971-3983 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:134 year:2024 number:7-8 day:09 month:09 pages:3971-3983 https://dx.doi.org/10.1007/s00170-024-14250-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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 52.70 VZ 52.74 VZ AR 134 2024 7-8 09 09 3971-3983
allfields_unstemmed	10.1007/s00170-024-14250-y doi (DE-627)SPR057299722 (SPR)s00170-024-14250-y-e DE-627 ger DE-627 rakwb eng 670 VZ 670 VZ 52.70 bkl 52.74 bkl He, Xiao verfasserin aut Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II 2024 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 2024. 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 To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time. Abrasive belt polishing machine (dpeaa)DE-He213 Crankshaft connecting rod journal (dpeaa)DE-He213 Process parameter optimization (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Li, Taifu verfasserin aut Li, Qiaoyue verfasserin aut Yang, Jie verfasserin aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 134(2024), 7-8 vom: 09. Sept., Seite 3971-3983 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:134 year:2024 number:7-8 day:09 month:09 pages:3971-3983 https://dx.doi.org/10.1007/s00170-024-14250-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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 52.70 VZ 52.74 VZ AR 134 2024 7-8 09 09 3971-3983
allfieldsGer	10.1007/s00170-024-14250-y doi (DE-627)SPR057299722 (SPR)s00170-024-14250-y-e DE-627 ger DE-627 rakwb eng 670 VZ 670 VZ 52.70 bkl 52.74 bkl He, Xiao verfasserin aut Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II 2024 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 2024. 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 To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time. Abrasive belt polishing machine (dpeaa)DE-He213 Crankshaft connecting rod journal (dpeaa)DE-He213 Process parameter optimization (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Li, Taifu verfasserin aut Li, Qiaoyue verfasserin aut Yang, Jie verfasserin aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 134(2024), 7-8 vom: 09. Sept., Seite 3971-3983 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:134 year:2024 number:7-8 day:09 month:09 pages:3971-3983 https://dx.doi.org/10.1007/s00170-024-14250-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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 52.70 VZ 52.74 VZ AR 134 2024 7-8 09 09 3971-3983
allfieldsSound	10.1007/s00170-024-14250-y doi (DE-627)SPR057299722 (SPR)s00170-024-14250-y-e DE-627 ger DE-627 rakwb eng 670 VZ 670 VZ 52.70 bkl 52.74 bkl He, Xiao verfasserin aut Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II 2024 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 2024. 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 To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time. Abrasive belt polishing machine (dpeaa)DE-He213 Crankshaft connecting rod journal (dpeaa)DE-He213 Process parameter optimization (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 Li, Taifu verfasserin aut Li, Qiaoyue verfasserin aut Yang, Jie verfasserin aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 134(2024), 7-8 vom: 09. Sept., Seite 3971-3983 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:134 year:2024 number:7-8 day:09 month:09 pages:3971-3983 https://dx.doi.org/10.1007/s00170-024-14250-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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 52.70 VZ 52.74 VZ AR 134 2024 7-8 09 09 3971-3983
language	English
source	Enthalten in The international journal of advanced manufacturing technology 134(2024), 7-8 vom: 09. Sept., Seite 3971-3983 volume:134 year:2024 number:7-8 day:09 month:09 pages:3971-3983
sourceStr	Enthalten in The international journal of advanced manufacturing technology 134(2024), 7-8 vom: 09. Sept., Seite 3971-3983 volume:134 year:2024 number:7-8 day:09 month:09 pages:3971-3983
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Abrasive belt polishing machine Crankshaft connecting rod journal Process parameter optimization BP neural network NSGA-II
dewey-raw	670
isfreeaccess_bool	false
container_title	The international journal of advanced manufacturing technology
authorswithroles_txt_mv	He, Xiao @@aut@@ Li, Taifu @@aut@@ Li, Qiaoyue @@aut@@ Yang, Jie @@aut@@
publishDateDaySort_date	2024-09-09T00:00:00Z
hierarchy_top_id	270127712
dewey-sort	3670
id	SPR057299722
language_de	englisch
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author	He, Xiao
spellingShingle	He, Xiao ddc 670 bkl 52.70 bkl 52.74 misc Abrasive belt polishing machine misc Crankshaft connecting rod journal misc Process parameter optimization misc BP neural network misc NSGA-II Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II
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topic_title	670 VZ 52.70 bkl 52.74 bkl Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II Abrasive belt polishing machine (dpeaa)DE-He213 Crankshaft connecting rod journal (dpeaa)DE-He213 Process parameter optimization (dpeaa)DE-He213 BP neural network (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213
topic	ddc 670 bkl 52.70 bkl 52.74 misc Abrasive belt polishing machine misc Crankshaft connecting rod journal misc Process parameter optimization misc BP neural network misc NSGA-II
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title	Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II
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title_full	Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II
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title_sort	optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on bp neural network and nsga-ii
title_auth	Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II
abstract	Abstract To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. 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 To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. 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 To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. 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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container_issue	7-8
title_short	Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II
url	https://dx.doi.org/10.1007/s00170-024-14250-y
remote_bool	true
author2	Li, Taifu Li, Qiaoyue Yang, Jie
author2Str	Li, Taifu Li, Qiaoyue Yang, Jie
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doi_str	10.1007/s00170-024-14250-y
up_date	2024-09-13T04:49:52.391Z
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Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II

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