Open Access
Manufacturing Rev.
Volume 2, 2015
Article Number 30
Number of page(s) 11
Published online 12 January 2016
  1. U.C. Okonkwo, C.O. Osueke, C.A.K. Ezugwu, Minimizing machining time in pocket milling based on optimal combination of the three basic prescription parameters, International Research Journal of Innovative Engineering 1 (2015) 12–34. [Google Scholar]
  2. T. Matsubara, H. Yamamoto, H. Mizumoto, Study on accuracy in end mill operations, Bulletin of the Japan Society of Precision Engineering 21 (1987) 95–100. [Google Scholar]
  3. E. Budak, Y. Altintas, Peripheral milling conditions for improved dimensional accuracy, International Journal of Machine Tools & Manufacture 34 (1994) 907–918. [CrossRef] [Google Scholar]
  4. T. Insperger, J. Gradišek, M. Kalveram, G. Stépán, E. Govekar, Machine tool chatter and surface location error in milling processes, Journal of Manufacturing Science and Engineering 128 (2006) 913. [CrossRef] [Google Scholar]
  5. M. Field, J.F. Kahles, W.P. Koster, Surface Finish and Surface Integrity, ASM Handbook, vol. 16, Machining, 9th edn., ASM Publication, ASM, Metal Park, Ohio, 1989. [Google Scholar]
  6. G. Peigne, H. Paris, D. Brissaud, A. Gouskov, Impact of the cutting dynamics of small radial immersion milling operations on machined surface roughness, International Journal of Machine Tools & Manufacture 44 (2004) 1133–1142. [CrossRef] [Google Scholar]
  7. H.A. Kishawy, M.E. Dumitrescu, M.A. Elbestawi, Effect of coolant strategy on tool performance chip morphology and surface quality during high-speed machining of A356 aluminum alloy, International Journal of Machine Tools & Manufacture 45 (2005) 219–227. [CrossRef] [Google Scholar]
  8. A.L. Mantle, D.K. Aspinwall, Surface integrity of a high speed milled gamma titanium aluminide, Journal of Materials Processing Technology 118 (2001) 143–150. [CrossRef] [Google Scholar]
  9. M.Y. Wang, H.Y. Chang, Experimental study of surface roughness in slot end milling, International Journal of Machine Tools & Manufacture 44 (2004) 51–57. [CrossRef] [Google Scholar]
  10. Y.H. Tsai, J.C. Chen, S.J. Lou, An in-process surface recognition system based on neural networks in end milling cutting operations, International Journal of Machine Tools & Manufacture 39 (1999) 583–605. [CrossRef] [Google Scholar]
  11. Y.M. Ertekin, Y. Kwon, T.L. Tseng, Identification of common sensory features for the control of CNC milling operations under varying cutting conditions, International Journal of Machine Tools & Manufacture 43 (2003) 897–904. [CrossRef] [Google Scholar]
  12. T.L. Ginta, A.K.M. Nurul Amin, H.C.D. Mohd Radzi, M.A. Lajis, Development of surface roughness models in end milling titanium alloy Ti-6Al-4V using uncoated tungsten carbide inserts, European Journal of Scientific Research 28 (2009) 542–551. [Google Scholar]
  13. R. Arokiadass, K. Palaniradja, N. Alagumoorthi, Predictive modeling of surface roughness in end milling of Al/SiCp metal matrix composite, Archives of Applied Science Research 3 (2011) 228–236. [Google Scholar]
  14. I.P. Okokpujie, U.C. Okonkwo, Effects of cutting parameters on surface roughness during end milling of aluminium under minimum quantity lubrication (MQL), International Journal of Science and Research 4 (2015) 2937–2942. [Google Scholar]
  15. L.B. Abhang, M. Hameedullah, Experimental investigation of minimum quantity lubricants in alloy steel turning, International Journal of Engineering Science and Technology 2 (2010) 3045–3053. [Google Scholar]
  16. N. Tosun, M. Huseyinoglu, Effect of MQL on surface roughness in milling of AA7075-T6, Materials and Manufacturing Processes 25 (2010) 793–798. [CrossRef] [Google Scholar]
  17. I. Korkut, M.A. Donertas, The influence of feed rate and cutting speed on the cutting forces, surface roughness and tool-chip contact length during face milling, Materials & Design 28 (2007) 308–312. [CrossRef] [Google Scholar]
  18. A.H. Suhail, N. Ismail, S.V. Wong, N.A. Abdul Jalil, Optimization of cutting parameters based on surface roughness and assistance of workpiece surface temperature in turning process, American Journal of Engineering and Applied Sciences 3 (2010) 102–108. [CrossRef] [Google Scholar]

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