Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach

Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality charac...
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Autor*in:	Ramakrishnan, H. [verfasserIn] Balasundaram, R. Selvaganapathy, P. Santhakumari, M. Sivasankaran, P. Vignesh, P.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Nano-lubrication ANOVA TOPSIS Al 7075 Machining

Anmerkung:	© Springer Nature Switzerland AG 2019

Übergeordnetes Werk:	Enthalten in: SN applied sciences - [Cham] : Springer International Publishing, 2019, 1(2019), 12 vom: 19. Nov.
Übergeordnetes Werk:	volume:1 ; year:2019 ; number:12 ; day:19 ; month:11

Links:	Volltext

DOI / URN:	10.1007/s42452-019-1664-0

Katalog-ID:	SPR03856842X

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520			\|a Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of $ Al_{2} %$ O_{3} $ of 5%. The turning experiments were carried out in Siemens—CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of $ Al_{2} %$ O_{3} $ nano lubrication gives better value of both MRR and SR.
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allfields	10.1007/s42452-019-1664-0 doi (DE-627)SPR03856842X (SPR)s42452-019-1664-0-e DE-627 ger DE-627 rakwb eng Ramakrishnan, H. verfasserin (orcid)0000-0002-9836-7526 aut Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of $ Al_{2} %$ O_{3} $ of 5%. The turning experiments were carried out in Siemens—CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of $ Al_{2} %$ O_{3} $ nano lubrication gives better value of both MRR and SR. Nano-lubrication (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 TOPSIS (dpeaa)DE-He213 Al 7075 (dpeaa)DE-He213 Machining (dpeaa)DE-He213 Balasundaram, R. aut Selvaganapathy, P. aut Santhakumari, M. aut Sivasankaran, P. aut Vignesh, P. aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 12 vom: 19. Nov. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:12 day:19 month:11 https://dx.doi.org/10.1007/s42452-019-1664-0 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 12 19 11
spelling	10.1007/s42452-019-1664-0 doi (DE-627)SPR03856842X (SPR)s42452-019-1664-0-e DE-627 ger DE-627 rakwb eng Ramakrishnan, H. verfasserin (orcid)0000-0002-9836-7526 aut Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of $ Al_{2} %$ O_{3} $ of 5%. The turning experiments were carried out in Siemens—CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of $ Al_{2} %$ O_{3} $ nano lubrication gives better value of both MRR and SR. Nano-lubrication (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 TOPSIS (dpeaa)DE-He213 Al 7075 (dpeaa)DE-He213 Machining (dpeaa)DE-He213 Balasundaram, R. aut Selvaganapathy, P. aut Santhakumari, M. aut Sivasankaran, P. aut Vignesh, P. aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 12 vom: 19. Nov. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:12 day:19 month:11 https://dx.doi.org/10.1007/s42452-019-1664-0 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 12 19 11
allfields_unstemmed	10.1007/s42452-019-1664-0 doi (DE-627)SPR03856842X (SPR)s42452-019-1664-0-e DE-627 ger DE-627 rakwb eng Ramakrishnan, H. verfasserin (orcid)0000-0002-9836-7526 aut Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of $ Al_{2} %$ O_{3} $ of 5%. The turning experiments were carried out in Siemens—CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of $ Al_{2} %$ O_{3} $ nano lubrication gives better value of both MRR and SR. Nano-lubrication (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 TOPSIS (dpeaa)DE-He213 Al 7075 (dpeaa)DE-He213 Machining (dpeaa)DE-He213 Balasundaram, R. aut Selvaganapathy, P. aut Santhakumari, M. aut Sivasankaran, P. aut Vignesh, P. aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 12 vom: 19. Nov. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:12 day:19 month:11 https://dx.doi.org/10.1007/s42452-019-1664-0 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 12 19 11
allfieldsGer	10.1007/s42452-019-1664-0 doi (DE-627)SPR03856842X (SPR)s42452-019-1664-0-e DE-627 ger DE-627 rakwb eng Ramakrishnan, H. verfasserin (orcid)0000-0002-9836-7526 aut Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of $ Al_{2} %$ O_{3} $ of 5%. The turning experiments were carried out in Siemens—CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of $ Al_{2} %$ O_{3} $ nano lubrication gives better value of both MRR and SR. Nano-lubrication (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 TOPSIS (dpeaa)DE-He213 Al 7075 (dpeaa)DE-He213 Machining (dpeaa)DE-He213 Balasundaram, R. aut Selvaganapathy, P. aut Santhakumari, M. aut Sivasankaran, P. aut Vignesh, P. aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 12 vom: 19. Nov. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:12 day:19 month:11 https://dx.doi.org/10.1007/s42452-019-1664-0 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 12 19 11
allfieldsSound	10.1007/s42452-019-1664-0 doi (DE-627)SPR03856842X (SPR)s42452-019-1664-0-e DE-627 ger DE-627 rakwb eng Ramakrishnan, H. verfasserin (orcid)0000-0002-9836-7526 aut Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of $ Al_{2} %$ O_{3} $ of 5%. The turning experiments were carried out in Siemens—CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of $ Al_{2} %$ O_{3} $ nano lubrication gives better value of both MRR and SR. Nano-lubrication (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 TOPSIS (dpeaa)DE-He213 Al 7075 (dpeaa)DE-He213 Machining (dpeaa)DE-He213 Balasundaram, R. aut Selvaganapathy, P. aut Santhakumari, M. aut Sivasankaran, P. aut Vignesh, P. aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 12 vom: 19. Nov. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:12 day:19 month:11 https://dx.doi.org/10.1007/s42452-019-1664-0 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 12 19 11
language	English
source	Enthalten in SN applied sciences 1(2019), 12 vom: 19. Nov. volume:1 year:2019 number:12 day:19 month:11
sourceStr	Enthalten in SN applied sciences 1(2019), 12 vom: 19. Nov. volume:1 year:2019 number:12 day:19 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Nano-lubrication ANOVA TOPSIS Al 7075 Machining
isfreeaccess_bool	false
container_title	SN applied sciences
authorswithroles_txt_mv	Ramakrishnan, H. @@aut@@ Balasundaram, R. @@aut@@ Selvaganapathy, P. @@aut@@ Santhakumari, M. @@aut@@ Sivasankaran, P. @@aut@@ Vignesh, P. @@aut@@
publishDateDaySort_date	2019-11-19T00:00:00Z
hierarchy_top_id	103761139X
id	SPR03856842X
language_de	englisch
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author	Ramakrishnan, H.
spellingShingle	Ramakrishnan, H. misc Nano-lubrication misc ANOVA misc TOPSIS misc Al 7075 misc Machining Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach
authorStr	Ramakrishnan, H.
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topic_title	Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach Nano-lubrication (dpeaa)DE-He213 ANOVA (dpeaa)DE-He213 TOPSIS (dpeaa)DE-He213 Al 7075 (dpeaa)DE-He213 Machining (dpeaa)DE-He213
topic	misc Nano-lubrication misc ANOVA misc TOPSIS misc Al 7075 misc Machining
topic_unstemmed	misc Nano-lubrication misc ANOVA misc TOPSIS misc Al 7075 misc Machining
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title	Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach
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title_full	Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach
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author_browse	Ramakrishnan, H. Balasundaram, R. Selvaganapathy, P. Santhakumari, M. Sivasankaran, P. Vignesh, P.
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title_sort	experimental investigation of turning al 7075 using $ al_{2} %$ o_{3} $ nano-cutting fluid: anova and topsis approach
title_auth	Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach
abstract	Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of $ Al_{2} %$ O_{3} $ of 5%. The turning experiments were carried out in Siemens—CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of $ Al_{2} %$ O_{3} $ nano lubrication gives better value of both MRR and SR. © Springer Nature Switzerland AG 2019
abstractGer	Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of $ Al_{2} %$ O_{3} $ of 5%. The turning experiments were carried out in Siemens—CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of $ Al_{2} %$ O_{3} $ nano lubrication gives better value of both MRR and SR. © Springer Nature Switzerland AG 2019
abstract_unstemmed	Abstract The evolution of modern industry tends to use aluminium based alloys due to its low density and high hardness. While machining aluminium, one of the major failure modes of cutting tool is the material being machined adheres to the tool cutting edge. This leads to poor surface quality characteristics. Tough different tool materials and tool coatings are available, achieving better machining parameter is still under research. Hence, in this work, Al 7075 is machined using CNC lathe under dry and with the nano lubricant of $ Al_{2} %$ O_{3} $ of 5%. The turning experiments were carried out in Siemens—CNC lathe to investigate the best operating conditions. There are 27 experiments based on full factorial approach is performed. The machining parameters are speed, feed and depth of cut. The output parameters are metal removal rate (MRR) and surface roughness (SR). The regression models developed from ANOVA are significant. To find best operating parameter TOPSIS is performed under each machining conditions. From the test results, it is concluded that the 1% of $ Al_{2} %$ O_{3} $ nano lubrication gives better value of both MRR and SR. © Springer Nature Switzerland AG 2019
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title_short	Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach
url	https://dx.doi.org/10.1007/s42452-019-1664-0
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up_date	2024-07-03T18:53:57.518Z
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Experimental investigation of turning Al 7075 using $ Al_{2} %$ O_{3} $ nano-cutting fluid: ANOVA and TOPSIS approach

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