Journal Press India^®

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The work and study presented aims to investigate the effect of the rake angles and cutting parameters (cutting speed, feed rate and depth of cut) on material removal rate (MRR) and surface roughness, in turning of Aluminium (6061) in dry condition. The cutting tools were prepared with positive Rake angles 20, 30, 40 .The effect of cutting condition (cutting speed and feed rate) on MRR and surface roughness were studied and analyzed while depth of cut kept constant as 0.2 mm. Design of experiments (DOE) were conducted for the analysis of the influence of the turning parameters on the surface roughness by using Taguchi design and then followed by Analysis of Variance (ANOVA) to find the maximum MRR and minimum surface roughness.

Keywords

Aluminium (6061); Rake Angle; Surface Roughness; Taguchi Method; Material Removal Rate (MRR)

1. P. Ananthakumar, M. Ramesh, Parameshwari, “Optimization Of Turning Process Parameters Using Multivariate Statistical Method-PCA Coupled With Taguchi Method International Journal of Scientific Engineering And Technology (ISSN : 2277-1581) 2 (4), PP : 263- 267 1, 2013 IJSET@2013 Page 263


2. Choudhury, M.A. El-Baradie, “Surface roughness prediction in the turning of high strength steel by factorial design of experiments”, Journal of Materials Processing technology, 67 (1997) 55- 67


3. D. Philip Selvaraj, P. Chandramohan, “Optimization Of Surface Roughness Of AISI 304 Austenitic Stainless Steel In Dry Turning Operation Using Taguchi Design Method”, Journal Of Engineering Science And Technology 5(3), 2010, 293 – 301


4. M. Kurt, E. Bagci, Y. Kaynal, “App. of Taguchi methods in the optimization of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes”, Int. Journal Advanced Manufacturing Technology, 40, 458-469, 2009


5. M. Kaladhar, K. V. Subbaiah, Ch. Srinivasa Rao,K. Narayana Rao, “Application of Taguchi Approach And Utility Concept In Solving The Multi-Objective Problem When Turning AISI 202 Austenitic Stainless Steel” Journal Of Engineering Science And Technology Review 4 (1) 55-61, 2011


6. Marinković Velibor, Madić Miloš, “Optimization Of Surface Roughness In Turning Alloy Steel By Innovation Website: www.ijari.com ISSN 2347-3258, 1(1), 13-21, 2013


7. Palanikumar, L. Karunamoorthy, R. Krathikeyan, “Assessment of factors influencing surface roughness on the machining of glass– reinforced polymer composites, Journal of Materials and Design”, 27 (2006) 862-871


8. Ranganath M S, Vipin, “Experimental Investigations on Surface Roughness for Turning of Alumunium (6061) Using Regression Analysis” Journal of Modeling and Simulation in Design and Manufacturing, 3(1&2), pp 190-196, 2013


9. Ranganath M S, Vipin, R S Mishra “Optimization Of Process Parameters In Turning Operation Of Aluminium (6061) With Cemented Carbide Inserts Using Taguchi Method And ANOVA” International Journal of Advance Research and


10. Ranganath M S, Vipin, “Optimization of Process Parameters in Turning Using Taguchi Method and ANOVA: A Review” International Journal of Advance Research and Innovation Website: www.ijari.com ISSN 2347-3258, 1(1), 31-45, 2013


11. Ranganath M S, Vipin, R S Mishra, “Neural Network Process Modelling for Turning of Using Taguchi Method” Scientific Research And Essays”, 6(16), Pp. 3474-3484, ISSN 1992-2248 ©2011 Academic Journals, 2011


12. Vipin, Harish Kumar, “Surface Roughness Prediction Model By Design Of Experiments For Turning Leaded Gun Metal” International Journal Of Applied Engineering Research ISSN 0973- 4562, 4 ,12 Pp. 2621–2628