Modeling and Assessment of Power Consumption for Green Machining Strategy
Abstract Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three...
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| Autor*in: |
Won, Jung-Jae [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Korean Society for Precision Engineering 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal of precision engineering and manufacturing-green technology - Berlin : Springer, 2014, 10(2022), 3 vom: 25. Aug., Seite 659-674 |
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| Übergeordnetes Werk: |
volume:10 ; year:2022 ; number:3 ; day:25 ; month:08 ; pages:659-674 |
| Links: |
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| DOI / URN: |
10.1007/s40684-022-00455-7 |
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SPR05016936X |
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| 520 | |a Abstract Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three perspectives, smart, optimal, and universal, are used to review the literature and define the strategic requirements. Based on the perspectives, the power consumption data was utilized to monitor the process in real-time and to control the process to be sustainable with a wide variety of cutting conditions and manufacturing environments. A power-prediction model was constructed, and two adaptive feed-control schemes were suggested. One controls the feed, while the other controls the feed per tooth. The experimental results show that both control schemes were up to 18% energy efficient with the given geometries and easily applicable over a wide range of conditions and satisfied the requirements set out above. The efficiencies of the control methods were discussed with respect to the control criteria, constraints, and materials. It is expected that this research will facilitate sustainable machining. | ||
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10.1007/s40684-022-00455-7 doi (DE-627)SPR05016936X (SPR)s40684-022-00455-7-e DE-627 ger DE-627 rakwb eng Won, Jung-Jae verfasserin (orcid)0000-0002-3410-6618 aut Modeling and Assessment of Power Consumption for Green Machining Strategy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2022. Springer Nature or its licensor 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 Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three perspectives, smart, optimal, and universal, are used to review the literature and define the strategic requirements. Based on the perspectives, the power consumption data was utilized to monitor the process in real-time and to control the process to be sustainable with a wide variety of cutting conditions and manufacturing environments. A power-prediction model was constructed, and two adaptive feed-control schemes were suggested. One controls the feed, while the other controls the feed per tooth. The experimental results show that both control schemes were up to 18% energy efficient with the given geometries and easily applicable over a wide range of conditions and satisfied the requirements set out above. The efficiencies of the control methods were discussed with respect to the control criteria, constraints, and materials. It is expected that this research will facilitate sustainable machining. Energy-saving (dpeaa)DE-He213 Time-saving (dpeaa)DE-He213 Specific energy consumption (dpeaa)DE-He213 Material removal rate (dpeaa)DE-He213 Machining strategy (dpeaa)DE-He213 Lee, Yong Ju (orcid)0000-0002-2777-7851 aut Hur, Yu-Jin aut Kim, Sang Won aut Yoon, Hae-Sung (orcid)0000-0002-9430-3541 aut Enthalten in International journal of precision engineering and manufacturing-green technology Berlin : Springer, 2014 10(2022), 3 vom: 25. Aug., Seite 659-674 (DE-627)780378865 (DE-600)2760378-7 2198-0810 nnns volume:10 year:2022 number:3 day:25 month:08 pages:659-674 https://dx.doi.org/10.1007/s40684-022-00455-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 3 25 08 659-674 |
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10.1007/s40684-022-00455-7 doi (DE-627)SPR05016936X (SPR)s40684-022-00455-7-e DE-627 ger DE-627 rakwb eng Won, Jung-Jae verfasserin (orcid)0000-0002-3410-6618 aut Modeling and Assessment of Power Consumption for Green Machining Strategy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2022. Springer Nature or its licensor 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 Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three perspectives, smart, optimal, and universal, are used to review the literature and define the strategic requirements. Based on the perspectives, the power consumption data was utilized to monitor the process in real-time and to control the process to be sustainable with a wide variety of cutting conditions and manufacturing environments. A power-prediction model was constructed, and two adaptive feed-control schemes were suggested. One controls the feed, while the other controls the feed per tooth. The experimental results show that both control schemes were up to 18% energy efficient with the given geometries and easily applicable over a wide range of conditions and satisfied the requirements set out above. The efficiencies of the control methods were discussed with respect to the control criteria, constraints, and materials. It is expected that this research will facilitate sustainable machining. Energy-saving (dpeaa)DE-He213 Time-saving (dpeaa)DE-He213 Specific energy consumption (dpeaa)DE-He213 Material removal rate (dpeaa)DE-He213 Machining strategy (dpeaa)DE-He213 Lee, Yong Ju (orcid)0000-0002-2777-7851 aut Hur, Yu-Jin aut Kim, Sang Won aut Yoon, Hae-Sung (orcid)0000-0002-9430-3541 aut Enthalten in International journal of precision engineering and manufacturing-green technology Berlin : Springer, 2014 10(2022), 3 vom: 25. Aug., Seite 659-674 (DE-627)780378865 (DE-600)2760378-7 2198-0810 nnns volume:10 year:2022 number:3 day:25 month:08 pages:659-674 https://dx.doi.org/10.1007/s40684-022-00455-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 3 25 08 659-674 |
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10.1007/s40684-022-00455-7 doi (DE-627)SPR05016936X (SPR)s40684-022-00455-7-e DE-627 ger DE-627 rakwb eng Won, Jung-Jae verfasserin (orcid)0000-0002-3410-6618 aut Modeling and Assessment of Power Consumption for Green Machining Strategy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2022. Springer Nature or its licensor 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 Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three perspectives, smart, optimal, and universal, are used to review the literature and define the strategic requirements. Based on the perspectives, the power consumption data was utilized to monitor the process in real-time and to control the process to be sustainable with a wide variety of cutting conditions and manufacturing environments. A power-prediction model was constructed, and two adaptive feed-control schemes were suggested. One controls the feed, while the other controls the feed per tooth. The experimental results show that both control schemes were up to 18% energy efficient with the given geometries and easily applicable over a wide range of conditions and satisfied the requirements set out above. The efficiencies of the control methods were discussed with respect to the control criteria, constraints, and materials. It is expected that this research will facilitate sustainable machining. Energy-saving (dpeaa)DE-He213 Time-saving (dpeaa)DE-He213 Specific energy consumption (dpeaa)DE-He213 Material removal rate (dpeaa)DE-He213 Machining strategy (dpeaa)DE-He213 Lee, Yong Ju (orcid)0000-0002-2777-7851 aut Hur, Yu-Jin aut Kim, Sang Won aut Yoon, Hae-Sung (orcid)0000-0002-9430-3541 aut Enthalten in International journal of precision engineering and manufacturing-green technology Berlin : Springer, 2014 10(2022), 3 vom: 25. Aug., Seite 659-674 (DE-627)780378865 (DE-600)2760378-7 2198-0810 nnns volume:10 year:2022 number:3 day:25 month:08 pages:659-674 https://dx.doi.org/10.1007/s40684-022-00455-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 3 25 08 659-674 |
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10.1007/s40684-022-00455-7 doi (DE-627)SPR05016936X (SPR)s40684-022-00455-7-e DE-627 ger DE-627 rakwb eng Won, Jung-Jae verfasserin (orcid)0000-0002-3410-6618 aut Modeling and Assessment of Power Consumption for Green Machining Strategy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2022. Springer Nature or its licensor 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 Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three perspectives, smart, optimal, and universal, are used to review the literature and define the strategic requirements. Based on the perspectives, the power consumption data was utilized to monitor the process in real-time and to control the process to be sustainable with a wide variety of cutting conditions and manufacturing environments. A power-prediction model was constructed, and two adaptive feed-control schemes were suggested. One controls the feed, while the other controls the feed per tooth. The experimental results show that both control schemes were up to 18% energy efficient with the given geometries and easily applicable over a wide range of conditions and satisfied the requirements set out above. The efficiencies of the control methods were discussed with respect to the control criteria, constraints, and materials. It is expected that this research will facilitate sustainable machining. Energy-saving (dpeaa)DE-He213 Time-saving (dpeaa)DE-He213 Specific energy consumption (dpeaa)DE-He213 Material removal rate (dpeaa)DE-He213 Machining strategy (dpeaa)DE-He213 Lee, Yong Ju (orcid)0000-0002-2777-7851 aut Hur, Yu-Jin aut Kim, Sang Won aut Yoon, Hae-Sung (orcid)0000-0002-9430-3541 aut Enthalten in International journal of precision engineering and manufacturing-green technology Berlin : Springer, 2014 10(2022), 3 vom: 25. Aug., Seite 659-674 (DE-627)780378865 (DE-600)2760378-7 2198-0810 nnns volume:10 year:2022 number:3 day:25 month:08 pages:659-674 https://dx.doi.org/10.1007/s40684-022-00455-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 3 25 08 659-674 |
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10.1007/s40684-022-00455-7 doi (DE-627)SPR05016936X (SPR)s40684-022-00455-7-e DE-627 ger DE-627 rakwb eng Won, Jung-Jae verfasserin (orcid)0000-0002-3410-6618 aut Modeling and Assessment of Power Consumption for Green Machining Strategy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2022. Springer Nature or its licensor 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 Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three perspectives, smart, optimal, and universal, are used to review the literature and define the strategic requirements. Based on the perspectives, the power consumption data was utilized to monitor the process in real-time and to control the process to be sustainable with a wide variety of cutting conditions and manufacturing environments. A power-prediction model was constructed, and two adaptive feed-control schemes were suggested. One controls the feed, while the other controls the feed per tooth. The experimental results show that both control schemes were up to 18% energy efficient with the given geometries and easily applicable over a wide range of conditions and satisfied the requirements set out above. The efficiencies of the control methods were discussed with respect to the control criteria, constraints, and materials. It is expected that this research will facilitate sustainable machining. Energy-saving (dpeaa)DE-He213 Time-saving (dpeaa)DE-He213 Specific energy consumption (dpeaa)DE-He213 Material removal rate (dpeaa)DE-He213 Machining strategy (dpeaa)DE-He213 Lee, Yong Ju (orcid)0000-0002-2777-7851 aut Hur, Yu-Jin aut Kim, Sang Won aut Yoon, Hae-Sung (orcid)0000-0002-9430-3541 aut Enthalten in International journal of precision engineering and manufacturing-green technology Berlin : Springer, 2014 10(2022), 3 vom: 25. Aug., Seite 659-674 (DE-627)780378865 (DE-600)2760378-7 2198-0810 nnns volume:10 year:2022 number:3 day:25 month:08 pages:659-674 https://dx.doi.org/10.1007/s40684-022-00455-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_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_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 3 25 08 659-674 |
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Won, Jung-Jae |
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modeling and assessment of power consumption for green machining strategy |
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Modeling and Assessment of Power Consumption for Green Machining Strategy |
| abstract |
Abstract Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three perspectives, smart, optimal, and universal, are used to review the literature and define the strategic requirements. Based on the perspectives, the power consumption data was utilized to monitor the process in real-time and to control the process to be sustainable with a wide variety of cutting conditions and manufacturing environments. A power-prediction model was constructed, and two adaptive feed-control schemes were suggested. One controls the feed, while the other controls the feed per tooth. The experimental results show that both control schemes were up to 18% energy efficient with the given geometries and easily applicable over a wide range of conditions and satisfied the requirements set out above. The efficiencies of the control methods were discussed with respect to the control criteria, constraints, and materials. It is expected that this research will facilitate sustainable machining. © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2022. Springer Nature or its licensor 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 Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three perspectives, smart, optimal, and universal, are used to review the literature and define the strategic requirements. Based on the perspectives, the power consumption data was utilized to monitor the process in real-time and to control the process to be sustainable with a wide variety of cutting conditions and manufacturing environments. A power-prediction model was constructed, and two adaptive feed-control schemes were suggested. One controls the feed, while the other controls the feed per tooth. The experimental results show that both control schemes were up to 18% energy efficient with the given geometries and easily applicable over a wide range of conditions and satisfied the requirements set out above. The efficiencies of the control methods were discussed with respect to the control criteria, constraints, and materials. It is expected that this research will facilitate sustainable machining. © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2022. Springer Nature or its licensor 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 Energy-saving technologies seek to minimize the environmental burden caused by manufacturing. In this study, it is aimed to develop a sustainable machining strategy that reduces the energy consumed during metal cutting, via modeling and assessment of power consumption of the process. Three perspectives, smart, optimal, and universal, are used to review the literature and define the strategic requirements. Based on the perspectives, the power consumption data was utilized to monitor the process in real-time and to control the process to be sustainable with a wide variety of cutting conditions and manufacturing environments. A power-prediction model was constructed, and two adaptive feed-control schemes were suggested. One controls the feed, while the other controls the feed per tooth. The experimental results show that both control schemes were up to 18% energy efficient with the given geometries and easily applicable over a wide range of conditions and satisfied the requirements set out above. The efficiencies of the control methods were discussed with respect to the control criteria, constraints, and materials. It is expected that this research will facilitate sustainable machining. © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2022. Springer Nature or its licensor 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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Modeling and Assessment of Power Consumption for Green Machining Strategy |
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https://dx.doi.org/10.1007/s40684-022-00455-7 |
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Lee, Yong Ju Hur, Yu-Jin Kim, Sang Won Yoon, Hae-Sung |
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Lee, Yong Ju Hur, Yu-Jin Kim, Sang Won Yoon, Hae-Sung |
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10.1007/s40684-022-00455-7 |
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2024-07-03T13:49:39.664Z |
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| score |
7.4005327 |