Research Article
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Year 2019, , 105 - 111, 30.11.2019
https://doi.org/10.22399/ijcesen.590692

Abstract

References

  • [1] Asilturk I, Neseli S, Ince MA, “Optimization of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods” Measurement. 78,120-128, 2016 DOI: 10.1016/j.measurement.2015.09.052
  • [2] Ozturk S, “Application of the Taguchi method for surface roughness predictions in the turning process” Materials Testing 58 (9), 782-787, 2016 DOI: 10.3139/120.110917
  • [3] Uysal A, “Surface roughness in nano fluid minimum quantity lubrication milling of AISI 430 ferritic stainless steel” Journal of Testing and Evaluation 45, 933-939, 2017 DOI: 10.1108/ILT-10-2015-0141
  • [4] Zhang JZ, Chen JC, Kirby ED, “Surface roughness optimization in an end-milling operation using the Taguchi design method” Journal of Materials Processing Technology 184, 233–239, 2007 DOI: 10.1016/j.jmatprotec.2006.11.029
  • [5] Pillai JU, Sanghrajka I, Shunmugavel M, Muthuramalingam T, Goldberg M, Littlefair G, “Optimisation of multiple response characteristics on end milling of aluminium alloy using Taguchi-Grey relational approach” Measurement 124, 291-298, 2018 DOI: 10.1016/j.measurement.2018.04.052
  • [6] Parida AK, Routara BC, Bhuyan RK, “Surface roughness model and parametric optimization in machining of GFRP composite: Taguchi and response surface methodology approach” Materials Today: Proceedings 2, 3065-3074, 2015 DOI: 10.1016/j.matpr.2015.07.247
  • [7] Tosun N, Kuru C, Altıntaş E, Erdin OE, “Investigation of surface roughness in milling with air and conventional cooling method” Journal of Faculty of Engineering and Architecture of Gazi University 25,141-146, 2010
  • [8] Günay M, “Optimization with Taguchi method of cutting parameters and tool nose radius in machining of AISI 316L steel” Journal of the Faculty of Engineering and Architecture of Gazi University 28, 437- 444, 2013
  • [9] Pinar AM, Filiz S, Ünlü BS, “A comparison of cooling methods in the pocket milling of AA5083-H36 alloy via Taguchi method” The International Journal of Advanced Manufacturing Technology 83, 1431–1440, 2016 DOI: 10.1007/s00170-015-7666-1
  • [10] Mandal N, Doloi B, Mondal B, Das R, “Optimization of flank wear using Zirconia Toughened Alumina (ZTA) cutting tool: Taguchi method and regression analysis” Measurement 4, 2149-2155, 2011 DOI: 10.1016/j.measurement.2011.07.022
  • [11] Kıvak T, “Optimization of surface roughness and flank wear using the Taguchi method in milling of hadfield steel with PVD and CVD coated inserts” Measurement 50, 19–28, 2014 DOI: 10.1016/j.measurement.2013.12.017
  • [12] Vishnu AV, Ramana MV, Tilak KBG, “Experimental investigations of process parameters influence on surface roughness in turning of EN-353 alloy steel under different machining environments” Materials Today: Proceedings 5, 4192–4200, 2018 DOI: 10.1016/j.matpr.2017.11.682
  • [13] Montgomery DC, “Design and Analysis of Experiments”, Wiley, New York, 2007
  • [14] Recioui A, “Optimization of circular antenna arrays using a differential search algorithm” Acta Physica Polonica A 128,7-8, 2015 DOI: 10.12693/APhysPolA.128.B-7
  • [15] Ozsoy M, Kurnaz C, “An optimization study of a hydraulic gear pump cover with finite element method” Acta Physica Polonica A 132, 944-948, 2017 DOI: 10.12693/APhysPolA.132.944
  • [16] Filiz İH, Olguner S, Evyapan E,” A study on optimization of planetary gear trains” Acta Physica Polonica A 132:728-733, 2017 DOI: 10.12693/APhysPolA.132.728 [17] Roy K R, “A Primer on the Taguchi Method”, Van Nostrand Reinhold, New York, 1990
  • [18] Gopal PM, Prakash KS, “Minimization of cutting force, temperature and surface roughness through GRA, TOPSIS and Taguchi techniques in end milling of Mg hybrid MMC” Measurement 116, 178-192, 2018 DOI: 10.1016/j.measurement.2017.11.011
  • [19] Ryan BF, Joiner BL, Cryer JD, MINITAB Handbook: Update for Release, Cengage Learning, 2012
  • [20] Cetin M H., Ozcelik B, Kuram E, Demirbas E,”Evaluation of vegetable based cutting fluids with extreme pressure and cutting parameters in turning of AISI 304L by Taguchi method” Journal of Cleaner Production 19, 2049-2056, 2011 DOI: 10.1016/j.jclepro.2011.07.013
  • [21] Kara F, “Taguchi optimization of surface roughness and flank wear during the turning of DIN 1.2344 tool steel” Materials Testing 59 (10) , 903-908, 2017 DOI: 10.3139/120.111085

Experimental Investigation of Surface Roughness of Cutting Parameters in T6 Aluminum Alloy Milling Process

Year 2019, , 105 - 111, 30.11.2019
https://doi.org/10.22399/ijcesen.590692

Abstract

In this study, optimum
machining conditions were determined by investigating the surface roughness of
the 7075-T6 aluminum alloy milling, depending on the spindle speed (rpm), feed
per tooth (Fz-mm/tooth) and the cooling type parameters. Taguchi experiment
design method was used to save time and cost. Experiments were based on the
Taguchi L16 orthogonal array and signal/noise (S/N) ratios were used in the
evaluation of the test results. Optimum surface roughness values were
determined with Taguchi optimization. In addition, variance analysis and
regression analysis were performed. Confirmation tests were conducted to verify
the work. As a result of the confirmation tests, it was found that the surface
roughness optimization in the milling of the 7075-T6 aluminium alloy was
successfully applied.

References

  • [1] Asilturk I, Neseli S, Ince MA, “Optimization of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods” Measurement. 78,120-128, 2016 DOI: 10.1016/j.measurement.2015.09.052
  • [2] Ozturk S, “Application of the Taguchi method for surface roughness predictions in the turning process” Materials Testing 58 (9), 782-787, 2016 DOI: 10.3139/120.110917
  • [3] Uysal A, “Surface roughness in nano fluid minimum quantity lubrication milling of AISI 430 ferritic stainless steel” Journal of Testing and Evaluation 45, 933-939, 2017 DOI: 10.1108/ILT-10-2015-0141
  • [4] Zhang JZ, Chen JC, Kirby ED, “Surface roughness optimization in an end-milling operation using the Taguchi design method” Journal of Materials Processing Technology 184, 233–239, 2007 DOI: 10.1016/j.jmatprotec.2006.11.029
  • [5] Pillai JU, Sanghrajka I, Shunmugavel M, Muthuramalingam T, Goldberg M, Littlefair G, “Optimisation of multiple response characteristics on end milling of aluminium alloy using Taguchi-Grey relational approach” Measurement 124, 291-298, 2018 DOI: 10.1016/j.measurement.2018.04.052
  • [6] Parida AK, Routara BC, Bhuyan RK, “Surface roughness model and parametric optimization in machining of GFRP composite: Taguchi and response surface methodology approach” Materials Today: Proceedings 2, 3065-3074, 2015 DOI: 10.1016/j.matpr.2015.07.247
  • [7] Tosun N, Kuru C, Altıntaş E, Erdin OE, “Investigation of surface roughness in milling with air and conventional cooling method” Journal of Faculty of Engineering and Architecture of Gazi University 25,141-146, 2010
  • [8] Günay M, “Optimization with Taguchi method of cutting parameters and tool nose radius in machining of AISI 316L steel” Journal of the Faculty of Engineering and Architecture of Gazi University 28, 437- 444, 2013
  • [9] Pinar AM, Filiz S, Ünlü BS, “A comparison of cooling methods in the pocket milling of AA5083-H36 alloy via Taguchi method” The International Journal of Advanced Manufacturing Technology 83, 1431–1440, 2016 DOI: 10.1007/s00170-015-7666-1
  • [10] Mandal N, Doloi B, Mondal B, Das R, “Optimization of flank wear using Zirconia Toughened Alumina (ZTA) cutting tool: Taguchi method and regression analysis” Measurement 4, 2149-2155, 2011 DOI: 10.1016/j.measurement.2011.07.022
  • [11] Kıvak T, “Optimization of surface roughness and flank wear using the Taguchi method in milling of hadfield steel with PVD and CVD coated inserts” Measurement 50, 19–28, 2014 DOI: 10.1016/j.measurement.2013.12.017
  • [12] Vishnu AV, Ramana MV, Tilak KBG, “Experimental investigations of process parameters influence on surface roughness in turning of EN-353 alloy steel under different machining environments” Materials Today: Proceedings 5, 4192–4200, 2018 DOI: 10.1016/j.matpr.2017.11.682
  • [13] Montgomery DC, “Design and Analysis of Experiments”, Wiley, New York, 2007
  • [14] Recioui A, “Optimization of circular antenna arrays using a differential search algorithm” Acta Physica Polonica A 128,7-8, 2015 DOI: 10.12693/APhysPolA.128.B-7
  • [15] Ozsoy M, Kurnaz C, “An optimization study of a hydraulic gear pump cover with finite element method” Acta Physica Polonica A 132, 944-948, 2017 DOI: 10.12693/APhysPolA.132.944
  • [16] Filiz İH, Olguner S, Evyapan E,” A study on optimization of planetary gear trains” Acta Physica Polonica A 132:728-733, 2017 DOI: 10.12693/APhysPolA.132.728 [17] Roy K R, “A Primer on the Taguchi Method”, Van Nostrand Reinhold, New York, 1990
  • [18] Gopal PM, Prakash KS, “Minimization of cutting force, temperature and surface roughness through GRA, TOPSIS and Taguchi techniques in end milling of Mg hybrid MMC” Measurement 116, 178-192, 2018 DOI: 10.1016/j.measurement.2017.11.011
  • [19] Ryan BF, Joiner BL, Cryer JD, MINITAB Handbook: Update for Release, Cengage Learning, 2012
  • [20] Cetin M H., Ozcelik B, Kuram E, Demirbas E,”Evaluation of vegetable based cutting fluids with extreme pressure and cutting parameters in turning of AISI 304L by Taguchi method” Journal of Cleaner Production 19, 2049-2056, 2011 DOI: 10.1016/j.jclepro.2011.07.013
  • [21] Kara F, “Taguchi optimization of surface roughness and flank wear during the turning of DIN 1.2344 tool steel” Materials Testing 59 (10) , 903-908, 2017 DOI: 10.3139/120.111085
There are 20 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Neslihan Özsoy 0000-0003-1546-0205

Publication Date November 30, 2019
Submission Date July 11, 2019
Acceptance Date September 23, 2019
Published in Issue Year 2019

Cite

APA Özsoy, N. (2019). Experimental Investigation of Surface Roughness of Cutting Parameters in T6 Aluminum Alloy Milling Process. International Journal of Computational and Experimental Science and Engineering, 5(3), 105-111. https://doi.org/10.22399/ijcesen.590692