Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles
Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry slid...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kurapati, Vijaya Bhaskar [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© The Indian Institute of Metals - IIM 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Transactions of the Indian Institute of Metals - [New Delhi] : Springer India, 2008, 71(2018), 7 vom: 13. Apr., Seite 1809-1825 |
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| Übergeordnetes Werk: |
volume:71 ; year:2018 ; number:7 ; day:13 ; month:04 ; pages:1809-1825 |
| Links: |
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| DOI / URN: |
10.1007/s12666-018-1322-z |
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| Katalog-ID: |
SPR026780828 |
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| 245 | 1 | 0 | |a Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles |
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| 520 | |a Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures. | ||
| 650 | 4 | |a Hybrid composites |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Dry sliding wear |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a ANOVA |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a RSM |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kommineni, Ravindra |4 aut | |
| 700 | 1 | |a Sundarrajan, Srinivasan |4 aut | |
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10.1007/s12666-018-1322-z doi (DE-627)SPR026780828 (SPR)s12666-018-1322-z-e DE-627 ger DE-627 rakwb eng Kurapati, Vijaya Bhaskar verfasserin (orcid)0000-0003-4191-352X aut Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Institute of Metals - IIM 2018 Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures. Hybrid composites (dpeaa)DE-He213 Dry sliding wear (dpeaa)DE-He213 Taguchi (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 RSM (dpeaa)DE-He213 Kommineni, Ravindra aut Sundarrajan, Srinivasan aut Enthalten in Transactions of the Indian Institute of Metals [New Delhi] : Springer India, 2008 71(2018), 7 vom: 13. Apr., Seite 1809-1825 (DE-627)617807884 (DE-600)2535335-4 0975-1645 nnns volume:71 year:2018 number:7 day:13 month:04 pages:1809-1825 https://dx.doi.org/10.1007/s12666-018-1322-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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 71 2018 7 13 04 1809-1825 |
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10.1007/s12666-018-1322-z doi (DE-627)SPR026780828 (SPR)s12666-018-1322-z-e DE-627 ger DE-627 rakwb eng Kurapati, Vijaya Bhaskar verfasserin (orcid)0000-0003-4191-352X aut Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Institute of Metals - IIM 2018 Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures. Hybrid composites (dpeaa)DE-He213 Dry sliding wear (dpeaa)DE-He213 Taguchi (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 RSM (dpeaa)DE-He213 Kommineni, Ravindra aut Sundarrajan, Srinivasan aut Enthalten in Transactions of the Indian Institute of Metals [New Delhi] : Springer India, 2008 71(2018), 7 vom: 13. Apr., Seite 1809-1825 (DE-627)617807884 (DE-600)2535335-4 0975-1645 nnns volume:71 year:2018 number:7 day:13 month:04 pages:1809-1825 https://dx.doi.org/10.1007/s12666-018-1322-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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 71 2018 7 13 04 1809-1825 |
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10.1007/s12666-018-1322-z doi (DE-627)SPR026780828 (SPR)s12666-018-1322-z-e DE-627 ger DE-627 rakwb eng Kurapati, Vijaya Bhaskar verfasserin (orcid)0000-0003-4191-352X aut Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Institute of Metals - IIM 2018 Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures. Hybrid composites (dpeaa)DE-He213 Dry sliding wear (dpeaa)DE-He213 Taguchi (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 RSM (dpeaa)DE-He213 Kommineni, Ravindra aut Sundarrajan, Srinivasan aut Enthalten in Transactions of the Indian Institute of Metals [New Delhi] : Springer India, 2008 71(2018), 7 vom: 13. Apr., Seite 1809-1825 (DE-627)617807884 (DE-600)2535335-4 0975-1645 nnns volume:71 year:2018 number:7 day:13 month:04 pages:1809-1825 https://dx.doi.org/10.1007/s12666-018-1322-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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 71 2018 7 13 04 1809-1825 |
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10.1007/s12666-018-1322-z doi (DE-627)SPR026780828 (SPR)s12666-018-1322-z-e DE-627 ger DE-627 rakwb eng Kurapati, Vijaya Bhaskar verfasserin (orcid)0000-0003-4191-352X aut Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Institute of Metals - IIM 2018 Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures. Hybrid composites (dpeaa)DE-He213 Dry sliding wear (dpeaa)DE-He213 Taguchi (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 RSM (dpeaa)DE-He213 Kommineni, Ravindra aut Sundarrajan, Srinivasan aut Enthalten in Transactions of the Indian Institute of Metals [New Delhi] : Springer India, 2008 71(2018), 7 vom: 13. Apr., Seite 1809-1825 (DE-627)617807884 (DE-600)2535335-4 0975-1645 nnns volume:71 year:2018 number:7 day:13 month:04 pages:1809-1825 https://dx.doi.org/10.1007/s12666-018-1322-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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 71 2018 7 13 04 1809-1825 |
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10.1007/s12666-018-1322-z doi (DE-627)SPR026780828 (SPR)s12666-018-1322-z-e DE-627 ger DE-627 rakwb eng Kurapati, Vijaya Bhaskar verfasserin (orcid)0000-0003-4191-352X aut Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Institute of Metals - IIM 2018 Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures. Hybrid composites (dpeaa)DE-He213 Dry sliding wear (dpeaa)DE-He213 Taguchi (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 RSM (dpeaa)DE-He213 Kommineni, Ravindra aut Sundarrajan, Srinivasan aut Enthalten in Transactions of the Indian Institute of Metals [New Delhi] : Springer India, 2008 71(2018), 7 vom: 13. Apr., Seite 1809-1825 (DE-627)617807884 (DE-600)2535335-4 0975-1645 nnns volume:71 year:2018 number:7 day:13 month:04 pages:1809-1825 https://dx.doi.org/10.1007/s12666-018-1322-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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 71 2018 7 13 04 1809-1825 |
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Kurapati, Vijaya Bhaskar @@aut@@ Kommineni, Ravindra @@aut@@ Sundarrajan, Srinivasan @@aut@@ |
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Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. 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| author |
Kurapati, Vijaya Bhaskar |
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Kurapati, Vijaya Bhaskar misc Hybrid composites misc Dry sliding wear misc Taguchi misc ANOVA misc RSM Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles |
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Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles Hybrid composites (dpeaa)DE-He213 Dry sliding wear (dpeaa)DE-He213 Taguchi (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 RSM (dpeaa)DE-He213 |
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misc Hybrid composites misc Dry sliding wear misc Taguchi misc ANOVA misc RSM |
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Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles |
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Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles |
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Kurapati, Vijaya Bhaskar |
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Kurapati, Vijaya Bhaskar Kommineni, Ravindra Sundarrajan, Srinivasan |
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statistical analysis and mathematical modeling of dry sliding wear parameters of 2024 aluminium hybrid composites reinforced with fly ash and sic particles |
| title_auth |
Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles |
| abstract |
Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures. © The Indian Institute of Metals - IIM 2018 |
| abstractGer |
Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures. © The Indian Institute of Metals - IIM 2018 |
| abstract_unstemmed |
Abstract The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi $ L_{27} $ orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures. © The Indian Institute of Metals - IIM 2018 |
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Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles |
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https://dx.doi.org/10.1007/s12666-018-1322-z |
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Kommineni, Ravindra Sundarrajan, Srinivasan |
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10.1007/s12666-018-1322-z |
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2024-07-03T22:44:26.526Z |
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|
| score |
7.400997 |