Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA)

Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of gree...
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Autor*in:	Ray, Amitava [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	EDM Principal component analysis Gray relational analysis Green manufacturing Multi-criteria decision-making

Anmerkung:	© Springer-Verlag London 2014

Übergeordnetes Werk:	Enthalten in: The international journal of advanced manufacturing technology - London : Springer, 1985, 87(2015), 5-8 vom: 24. Apr., Seite 1299-1311
Übergeordnetes Werk:	volume:87 ; year:2015 ; number:5-8 ; day:24 ; month:04 ; pages:1299-1311

Links:	Volltext

DOI / URN:	10.1007/s00170-014-6372-8

Katalog-ID:	SPR001897969

Internformat


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520			\|a Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of green electrical discharge machining (EDM). The major performance characteristics selected are process time, relative tool wear ratio, process energy, concentration of aerosol, and dielectric consumption. The corresponding machining parameters are peak current, pulse duration, dielectric level, and flushing pressure. Initially, Taguchi (L9) orthogonal array has been used to perform the experimental runs and the optimal process parameters using the GRA approach. The weighting values corresponding to various performance characteristics are determined using PCA. Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters; the peak current is the most influencing parameter having 52.87 % of contribution followed by flushing pressure, dielectric level, and pulse duration with 22.00, 21.52, and 3.55 %, respectively. Finally, multiple regression analysis is performed to determine the relationship between machining parameters and performance characteristics. The Fuzzy-TOPSIS and VIKOR methodologies have been used to compare the results of the proposed methodology, and the optimum process parameters obtained are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm), and flushing pressure (0.3 kg/$ cm^{2} $).
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allfields	10.1007/s00170-014-6372-8 doi (DE-627)SPR001897969 (SPR)s00170-014-6372-8-e DE-627 ger DE-627 rakwb eng Ray, Amitava verfasserin aut Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA) 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2014 Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of green electrical discharge machining (EDM). The major performance characteristics selected are process time, relative tool wear ratio, process energy, concentration of aerosol, and dielectric consumption. The corresponding machining parameters are peak current, pulse duration, dielectric level, and flushing pressure. Initially, Taguchi (L9) orthogonal array has been used to perform the experimental runs and the optimal process parameters using the GRA approach. The weighting values corresponding to various performance characteristics are determined using PCA. Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters; the peak current is the most influencing parameter having 52.87 % of contribution followed by flushing pressure, dielectric level, and pulse duration with 22.00, 21.52, and 3.55 %, respectively. Finally, multiple regression analysis is performed to determine the relationship between machining parameters and performance characteristics. The Fuzzy-TOPSIS and VIKOR methodologies have been used to compare the results of the proposed methodology, and the optimum process parameters obtained are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm), and flushing pressure (0.3 kg/$ cm^{2} $). EDM (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Gray relational analysis (dpeaa)DE-He213 Green manufacturing (dpeaa)DE-He213 Multi-criteria decision-making (dpeaa)DE-He213 Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 87(2015), 5-8 vom: 24. Apr., Seite 1299-1311 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:87 year:2015 number:5-8 day:24 month:04 pages:1299-1311 https://dx.doi.org/10.1007/s00170-014-6372-8 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_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_206 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_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2015 5-8 24 04 1299-1311
spelling	10.1007/s00170-014-6372-8 doi (DE-627)SPR001897969 (SPR)s00170-014-6372-8-e DE-627 ger DE-627 rakwb eng Ray, Amitava verfasserin aut Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA) 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2014 Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of green electrical discharge machining (EDM). The major performance characteristics selected are process time, relative tool wear ratio, process energy, concentration of aerosol, and dielectric consumption. The corresponding machining parameters are peak current, pulse duration, dielectric level, and flushing pressure. Initially, Taguchi (L9) orthogonal array has been used to perform the experimental runs and the optimal process parameters using the GRA approach. The weighting values corresponding to various performance characteristics are determined using PCA. Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters; the peak current is the most influencing parameter having 52.87 % of contribution followed by flushing pressure, dielectric level, and pulse duration with 22.00, 21.52, and 3.55 %, respectively. Finally, multiple regression analysis is performed to determine the relationship between machining parameters and performance characteristics. The Fuzzy-TOPSIS and VIKOR methodologies have been used to compare the results of the proposed methodology, and the optimum process parameters obtained are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm), and flushing pressure (0.3 kg/$ cm^{2} $). EDM (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Gray relational analysis (dpeaa)DE-He213 Green manufacturing (dpeaa)DE-He213 Multi-criteria decision-making (dpeaa)DE-He213 Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 87(2015), 5-8 vom: 24. Apr., Seite 1299-1311 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:87 year:2015 number:5-8 day:24 month:04 pages:1299-1311 https://dx.doi.org/10.1007/s00170-014-6372-8 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_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_206 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_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2015 5-8 24 04 1299-1311
allfields_unstemmed	10.1007/s00170-014-6372-8 doi (DE-627)SPR001897969 (SPR)s00170-014-6372-8-e DE-627 ger DE-627 rakwb eng Ray, Amitava verfasserin aut Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA) 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2014 Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of green electrical discharge machining (EDM). The major performance characteristics selected are process time, relative tool wear ratio, process energy, concentration of aerosol, and dielectric consumption. The corresponding machining parameters are peak current, pulse duration, dielectric level, and flushing pressure. Initially, Taguchi (L9) orthogonal array has been used to perform the experimental runs and the optimal process parameters using the GRA approach. The weighting values corresponding to various performance characteristics are determined using PCA. Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters; the peak current is the most influencing parameter having 52.87 % of contribution followed by flushing pressure, dielectric level, and pulse duration with 22.00, 21.52, and 3.55 %, respectively. Finally, multiple regression analysis is performed to determine the relationship between machining parameters and performance characteristics. The Fuzzy-TOPSIS and VIKOR methodologies have been used to compare the results of the proposed methodology, and the optimum process parameters obtained are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm), and flushing pressure (0.3 kg/$ cm^{2} $). EDM (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Gray relational analysis (dpeaa)DE-He213 Green manufacturing (dpeaa)DE-He213 Multi-criteria decision-making (dpeaa)DE-He213 Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 87(2015), 5-8 vom: 24. Apr., Seite 1299-1311 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:87 year:2015 number:5-8 day:24 month:04 pages:1299-1311 https://dx.doi.org/10.1007/s00170-014-6372-8 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_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_206 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_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2015 5-8 24 04 1299-1311
allfieldsGer	10.1007/s00170-014-6372-8 doi (DE-627)SPR001897969 (SPR)s00170-014-6372-8-e DE-627 ger DE-627 rakwb eng Ray, Amitava verfasserin aut Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA) 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2014 Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of green electrical discharge machining (EDM). The major performance characteristics selected are process time, relative tool wear ratio, process energy, concentration of aerosol, and dielectric consumption. The corresponding machining parameters are peak current, pulse duration, dielectric level, and flushing pressure. Initially, Taguchi (L9) orthogonal array has been used to perform the experimental runs and the optimal process parameters using the GRA approach. The weighting values corresponding to various performance characteristics are determined using PCA. Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters; the peak current is the most influencing parameter having 52.87 % of contribution followed by flushing pressure, dielectric level, and pulse duration with 22.00, 21.52, and 3.55 %, respectively. Finally, multiple regression analysis is performed to determine the relationship between machining parameters and performance characteristics. The Fuzzy-TOPSIS and VIKOR methodologies have been used to compare the results of the proposed methodology, and the optimum process parameters obtained are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm), and flushing pressure (0.3 kg/$ cm^{2} $). EDM (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Gray relational analysis (dpeaa)DE-He213 Green manufacturing (dpeaa)DE-He213 Multi-criteria decision-making (dpeaa)DE-He213 Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 87(2015), 5-8 vom: 24. Apr., Seite 1299-1311 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:87 year:2015 number:5-8 day:24 month:04 pages:1299-1311 https://dx.doi.org/10.1007/s00170-014-6372-8 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_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_206 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_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2015 5-8 24 04 1299-1311
allfieldsSound	10.1007/s00170-014-6372-8 doi (DE-627)SPR001897969 (SPR)s00170-014-6372-8-e DE-627 ger DE-627 rakwb eng Ray, Amitava verfasserin aut Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA) 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2014 Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of green electrical discharge machining (EDM). The major performance characteristics selected are process time, relative tool wear ratio, process energy, concentration of aerosol, and dielectric consumption. The corresponding machining parameters are peak current, pulse duration, dielectric level, and flushing pressure. Initially, Taguchi (L9) orthogonal array has been used to perform the experimental runs and the optimal process parameters using the GRA approach. The weighting values corresponding to various performance characteristics are determined using PCA. Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters; the peak current is the most influencing parameter having 52.87 % of contribution followed by flushing pressure, dielectric level, and pulse duration with 22.00, 21.52, and 3.55 %, respectively. Finally, multiple regression analysis is performed to determine the relationship between machining parameters and performance characteristics. The Fuzzy-TOPSIS and VIKOR methodologies have been used to compare the results of the proposed methodology, and the optimum process parameters obtained are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm), and flushing pressure (0.3 kg/$ cm^{2} $). EDM (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Gray relational analysis (dpeaa)DE-He213 Green manufacturing (dpeaa)DE-He213 Multi-criteria decision-making (dpeaa)DE-He213 Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 87(2015), 5-8 vom: 24. Apr., Seite 1299-1311 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:87 year:2015 number:5-8 day:24 month:04 pages:1299-1311 https://dx.doi.org/10.1007/s00170-014-6372-8 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_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_206 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_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2015 5-8 24 04 1299-1311
language	English
source	Enthalten in The international journal of advanced manufacturing technology 87(2015), 5-8 vom: 24. Apr., Seite 1299-1311 volume:87 year:2015 number:5-8 day:24 month:04 pages:1299-1311
sourceStr	Enthalten in The international journal of advanced manufacturing technology 87(2015), 5-8 vom: 24. Apr., Seite 1299-1311 volume:87 year:2015 number:5-8 day:24 month:04 pages:1299-1311
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	EDM Principal component analysis Gray relational analysis Green manufacturing Multi-criteria decision-making
isfreeaccess_bool	false
container_title	The international journal of advanced manufacturing technology
authorswithroles_txt_mv	Ray, Amitava @@aut@@
publishDateDaySort_date	2015-04-24T00:00:00Z
hierarchy_top_id	270127712
id	SPR001897969
language_de	englisch
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author	Ray, Amitava
spellingShingle	Ray, Amitava misc EDM misc Principal component analysis misc Gray relational analysis misc Green manufacturing misc Multi-criteria decision-making Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA)
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topic_title	Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA) EDM (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Gray relational analysis (dpeaa)DE-He213 Green manufacturing (dpeaa)DE-He213 Multi-criteria decision-making (dpeaa)DE-He213
topic	misc EDM misc Principal component analysis misc Gray relational analysis misc Green manufacturing misc Multi-criteria decision-making
topic_unstemmed	misc EDM misc Principal component analysis misc Gray relational analysis misc Green manufacturing misc Multi-criteria decision-making
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title	Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA)
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title_full	Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA)
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title_sort	optimization of process parameters of green electrical discharge machining using principal component analysis (pca)
title_auth	Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA)
abstract	Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of green electrical discharge machining (EDM). The major performance characteristics selected are process time, relative tool wear ratio, process energy, concentration of aerosol, and dielectric consumption. The corresponding machining parameters are peak current, pulse duration, dielectric level, and flushing pressure. Initially, Taguchi (L9) orthogonal array has been used to perform the experimental runs and the optimal process parameters using the GRA approach. The weighting values corresponding to various performance characteristics are determined using PCA. Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters; the peak current is the most influencing parameter having 52.87 % of contribution followed by flushing pressure, dielectric level, and pulse duration with 22.00, 21.52, and 3.55 %, respectively. Finally, multiple regression analysis is performed to determine the relationship between machining parameters and performance characteristics. The Fuzzy-TOPSIS and VIKOR methodologies have been used to compare the results of the proposed methodology, and the optimum process parameters obtained are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm), and flushing pressure (0.3 kg/$ cm^{2} $). © Springer-Verlag London 2014
abstractGer	Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of green electrical discharge machining (EDM). The major performance characteristics selected are process time, relative tool wear ratio, process energy, concentration of aerosol, and dielectric consumption. The corresponding machining parameters are peak current, pulse duration, dielectric level, and flushing pressure. Initially, Taguchi (L9) orthogonal array has been used to perform the experimental runs and the optimal process parameters using the GRA approach. The weighting values corresponding to various performance characteristics are determined using PCA. Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters; the peak current is the most influencing parameter having 52.87 % of contribution followed by flushing pressure, dielectric level, and pulse duration with 22.00, 21.52, and 3.55 %, respectively. Finally, multiple regression analysis is performed to determine the relationship between machining parameters and performance characteristics. The Fuzzy-TOPSIS and VIKOR methodologies have been used to compare the results of the proposed methodology, and the optimum process parameters obtained are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm), and flushing pressure (0.3 kg/$ cm^{2} $). © Springer-Verlag London 2014
abstract_unstemmed	Abstract The objective of this research is to solve the multi-response parameter optimization problems of green manufacturing. A combination of gray relational analysis (GRA) associated with principal component analysis (PCA) method has been developed and has optimized the process parameters of green electrical discharge machining (EDM). The major performance characteristics selected are process time, relative tool wear ratio, process energy, concentration of aerosol, and dielectric consumption. The corresponding machining parameters are peak current, pulse duration, dielectric level, and flushing pressure. Initially, Taguchi (L9) orthogonal array has been used to perform the experimental runs and the optimal process parameters using the GRA approach. The weighting values corresponding to various performance characteristics are determined using PCA. Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters; the peak current is the most influencing parameter having 52.87 % of contribution followed by flushing pressure, dielectric level, and pulse duration with 22.00, 21.52, and 3.55 %, respectively. Finally, multiple regression analysis is performed to determine the relationship between machining parameters and performance characteristics. The Fuzzy-TOPSIS and VIKOR methodologies have been used to compare the results of the proposed methodology, and the optimum process parameters obtained are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm), and flushing pressure (0.3 kg/$ cm^{2} $). © Springer-Verlag London 2014
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title_short	Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA)
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Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA)

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