Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis

Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experiment...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Singh, Lakhwinder [verfasserIn] Khan, R. A. Aggarwal, M. L.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	ANOVA Gray relational analysis (GRA) Optimization Shot peening Taguchi method

Anmerkung:	© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012

Übergeordnetes Werk:	Enthalten in: Journal of mechanical science and technology - Berlin : Springer, 2005, 26(2012), 6 vom: Juni, Seite 1731-1739
Übergeordnetes Werk:	volume:26 ; year:2012 ; number:6 ; month:06 ; pages:1731-1739

Links:	Volltext

DOI / URN:	10.1007/s12206-012-0434-3

Katalog-ID:	SPR025303198

Internformat


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allfields	10.1007/s12206-012-0434-3 doi (DE-627)SPR025303198 (SPR)s12206-012-0434-3-e DE-627 ger DE-627 rakwb eng Singh, Lakhwinder verfasserin aut Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experimental runs are performed to determine best process parameters level. An analysis of variance (ANOVA) is carried out to identify significant peening parameters. The response tables are obtained for analyzing the optimal levels of shot peening parameters and major factors that affect the quality function. The multiple performance characteristics including tensile strength, surface hardness and surface roughness are the quality functions considered for the optimization. Further mathematical models are developed using regression analysis for the tensile strength, surface hardness and surface roughness. It will be very helpful to the engineers in deciding the levels of the shot peening parameters for desired performance characteristics. ANOVA (dpeaa)DE-He213 Gray relational analysis (GRA) (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Shot peening (dpeaa)DE-He213 Taguchi method (dpeaa)DE-He213 Khan, R. A. aut Aggarwal, M. L. aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 26(2012), 6 vom: Juni, Seite 1731-1739 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:26 year:2012 number:6 month:06 pages:1731-1739 https://dx.doi.org/10.1007/s12206-012-0434-3 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_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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2012 6 06 1731-1739
spelling	10.1007/s12206-012-0434-3 doi (DE-627)SPR025303198 (SPR)s12206-012-0434-3-e DE-627 ger DE-627 rakwb eng Singh, Lakhwinder verfasserin aut Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experimental runs are performed to determine best process parameters level. An analysis of variance (ANOVA) is carried out to identify significant peening parameters. The response tables are obtained for analyzing the optimal levels of shot peening parameters and major factors that affect the quality function. The multiple performance characteristics including tensile strength, surface hardness and surface roughness are the quality functions considered for the optimization. Further mathematical models are developed using regression analysis for the tensile strength, surface hardness and surface roughness. It will be very helpful to the engineers in deciding the levels of the shot peening parameters for desired performance characteristics. ANOVA (dpeaa)DE-He213 Gray relational analysis (GRA) (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Shot peening (dpeaa)DE-He213 Taguchi method (dpeaa)DE-He213 Khan, R. A. aut Aggarwal, M. L. aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 26(2012), 6 vom: Juni, Seite 1731-1739 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:26 year:2012 number:6 month:06 pages:1731-1739 https://dx.doi.org/10.1007/s12206-012-0434-3 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_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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2012 6 06 1731-1739
allfields_unstemmed	10.1007/s12206-012-0434-3 doi (DE-627)SPR025303198 (SPR)s12206-012-0434-3-e DE-627 ger DE-627 rakwb eng Singh, Lakhwinder verfasserin aut Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experimental runs are performed to determine best process parameters level. An analysis of variance (ANOVA) is carried out to identify significant peening parameters. The response tables are obtained for analyzing the optimal levels of shot peening parameters and major factors that affect the quality function. The multiple performance characteristics including tensile strength, surface hardness and surface roughness are the quality functions considered for the optimization. Further mathematical models are developed using regression analysis for the tensile strength, surface hardness and surface roughness. It will be very helpful to the engineers in deciding the levels of the shot peening parameters for desired performance characteristics. ANOVA (dpeaa)DE-He213 Gray relational analysis (GRA) (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Shot peening (dpeaa)DE-He213 Taguchi method (dpeaa)DE-He213 Khan, R. A. aut Aggarwal, M. L. aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 26(2012), 6 vom: Juni, Seite 1731-1739 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:26 year:2012 number:6 month:06 pages:1731-1739 https://dx.doi.org/10.1007/s12206-012-0434-3 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_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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2012 6 06 1731-1739
allfieldsGer	10.1007/s12206-012-0434-3 doi (DE-627)SPR025303198 (SPR)s12206-012-0434-3-e DE-627 ger DE-627 rakwb eng Singh, Lakhwinder verfasserin aut Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experimental runs are performed to determine best process parameters level. An analysis of variance (ANOVA) is carried out to identify significant peening parameters. The response tables are obtained for analyzing the optimal levels of shot peening parameters and major factors that affect the quality function. The multiple performance characteristics including tensile strength, surface hardness and surface roughness are the quality functions considered for the optimization. Further mathematical models are developed using regression analysis for the tensile strength, surface hardness and surface roughness. It will be very helpful to the engineers in deciding the levels of the shot peening parameters for desired performance characteristics. ANOVA (dpeaa)DE-He213 Gray relational analysis (GRA) (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Shot peening (dpeaa)DE-He213 Taguchi method (dpeaa)DE-He213 Khan, R. A. aut Aggarwal, M. L. aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 26(2012), 6 vom: Juni, Seite 1731-1739 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:26 year:2012 number:6 month:06 pages:1731-1739 https://dx.doi.org/10.1007/s12206-012-0434-3 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_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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2012 6 06 1731-1739
allfieldsSound	10.1007/s12206-012-0434-3 doi (DE-627)SPR025303198 (SPR)s12206-012-0434-3-e DE-627 ger DE-627 rakwb eng Singh, Lakhwinder verfasserin aut Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experimental runs are performed to determine best process parameters level. An analysis of variance (ANOVA) is carried out to identify significant peening parameters. The response tables are obtained for analyzing the optimal levels of shot peening parameters and major factors that affect the quality function. The multiple performance characteristics including tensile strength, surface hardness and surface roughness are the quality functions considered for the optimization. Further mathematical models are developed using regression analysis for the tensile strength, surface hardness and surface roughness. It will be very helpful to the engineers in deciding the levels of the shot peening parameters for desired performance characteristics. ANOVA (dpeaa)DE-He213 Gray relational analysis (GRA) (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Shot peening (dpeaa)DE-He213 Taguchi method (dpeaa)DE-He213 Khan, R. A. aut Aggarwal, M. L. aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 26(2012), 6 vom: Juni, Seite 1731-1739 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:26 year:2012 number:6 month:06 pages:1731-1739 https://dx.doi.org/10.1007/s12206-012-0434-3 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_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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2012 6 06 1731-1739
language	English
source	Enthalten in Journal of mechanical science and technology 26(2012), 6 vom: Juni, Seite 1731-1739 volume:26 year:2012 number:6 month:06 pages:1731-1739
sourceStr	Enthalten in Journal of mechanical science and technology 26(2012), 6 vom: Juni, Seite 1731-1739 volume:26 year:2012 number:6 month:06 pages:1731-1739
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	ANOVA Gray relational analysis (GRA) Optimization Shot peening Taguchi method
isfreeaccess_bool	false
container_title	Journal of mechanical science and technology
authorswithroles_txt_mv	Singh, Lakhwinder @@aut@@ Khan, R. A. @@aut@@ Aggarwal, M. L. @@aut@@
publishDateDaySort_date	2012-06-01T00:00:00Z
hierarchy_top_id	58714016X
id	SPR025303198
language_de	englisch
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author	Singh, Lakhwinder
spellingShingle	Singh, Lakhwinder misc ANOVA misc Gray relational analysis (GRA) misc Optimization misc Shot peening misc Taguchi method Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis
authorStr	Singh, Lakhwinder
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topic_title	Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis ANOVA (dpeaa)DE-He213 Gray relational analysis (GRA) (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Shot peening (dpeaa)DE-He213 Taguchi method (dpeaa)DE-He213
topic	misc ANOVA misc Gray relational analysis (GRA) misc Optimization misc Shot peening misc Taguchi method
topic_unstemmed	misc ANOVA misc Gray relational analysis (GRA) misc Optimization misc Shot peening misc Taguchi method
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title	Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis
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title_full	Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis
author_sort	Singh, Lakhwinder
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author_browse	Singh, Lakhwinder Khan, R. A. Aggarwal, M. L.
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doi_str_mv	10.1007/s12206-012-0434-3
title_sort	empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis
title_auth	Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis
abstract	Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experimental runs are performed to determine best process parameters level. An analysis of variance (ANOVA) is carried out to identify significant peening parameters. The response tables are obtained for analyzing the optimal levels of shot peening parameters and major factors that affect the quality function. The multiple performance characteristics including tensile strength, surface hardness and surface roughness are the quality functions considered for the optimization. Further mathematical models are developed using regression analysis for the tensile strength, surface hardness and surface roughness. It will be very helpful to the engineers in deciding the levels of the shot peening parameters for desired performance characteristics. © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012
abstractGer	Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experimental runs are performed to determine best process parameters level. An analysis of variance (ANOVA) is carried out to identify significant peening parameters. The response tables are obtained for analyzing the optimal levels of shot peening parameters and major factors that affect the quality function. The multiple performance characteristics including tensile strength, surface hardness and surface roughness are the quality functions considered for the optimization. Further mathematical models are developed using regression analysis for the tensile strength, surface hardness and surface roughness. It will be very helpful to the engineers in deciding the levels of the shot peening parameters for desired performance characteristics. © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012
abstract_unstemmed	Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experimental runs are performed to determine best process parameters level. An analysis of variance (ANOVA) is carried out to identify significant peening parameters. The response tables are obtained for analyzing the optimal levels of shot peening parameters and major factors that affect the quality function. The multiple performance characteristics including tensile strength, surface hardness and surface roughness are the quality functions considered for the optimization. Further mathematical models are developed using regression analysis for the tensile strength, surface hardness and surface roughness. It will be very helpful to the engineers in deciding the levels of the shot peening parameters for desired performance characteristics. © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012
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title_short	Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis
url	https://dx.doi.org/10.1007/s12206-012-0434-3
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up_date	2024-07-03T15:09:19.174Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR025303198</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230403065310.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2012 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12206-012-0434-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR025303198</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12206-012-0434-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Singh, Lakhwinder</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The attempt of this paper is to present an effective approach for the optimization of the shot peening process of welded AISI 304 austenitic stainless steel with multi performance characteristics using Grey relational analysis (GRA) based on Taguchi orthogonal array. Twenty-seven experimental runs are performed to determine best process parameters level. An analysis of variance (ANOVA) is carried out to identify significant peening parameters. The response tables are obtained for analyzing the optimal levels of shot peening parameters and major factors that affect the quality function. The multiple performance characteristics including tensile strength, surface hardness and surface roughness are the quality functions considered for the optimization. Further mathematical models are developed using regression analysis for the tensile strength, surface hardness and surface roughness. It will be very helpful to the engineers in deciding the levels of the shot peening parameters for desired performance characteristics.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ANOVA</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gray relational analysis (GRA)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Shot peening</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Taguchi method</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Khan, R. 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Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis

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