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All issues Volume 17 / No 3 (2016) Mechanics & Industry, 17 3 (2016) 309 References
Open Access
Issue
Mechanics & Industry
Volume 17, Number 3, 2016
Article Number 309
Number of page(s) 12
DOI https://doi.org/10.1051/meca/2015076
Published online 15 February 2016
  1. B. Fnides, M.A. Yallese, H. Aouici, Hard turning of hot work steel AISI H11: Evaluation of cutting pressures, resulting force and temperature, Mechanika 4 (2008) 59–63 [Google Scholar]
  2. H. Aouici, M. A Yallese, B Fnides, K. Chaoui, T Mabrouki, Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool, J. Mech. Sci. Technol. 25 (2011) 2843–2851 [CrossRef] [Google Scholar]
  3. J.M. Zou, M. Anderson, J.E. Stahl, Identification of cutting errors in precision hard turning process, J. Mater. Process. Technol. 15–154 (2004) 746–750 [Google Scholar]
  4. J. Rech, A. Moisan, Surface Integrity in finish hard turning of case hardened steels, J. Mach. Tools Manuf. 43 (2003) 54–550 [CrossRef] [Google Scholar]
  5. I. Meddour, M.A. Yallese, R. Khattabi, M. Elbah, L. Boulanoua, Investigation and modeling of cutting forces and surface roughness when hard turning of AISI 52100 steel with mixed ceramic tool: cutting conditions optimization, Int. J. Adv. Manufact. Technol. 77 (2014) 1387–1399 [CrossRef] [Google Scholar]
  6. J.S. Dureja, V.K. Gupta, M. Dogra, Design optimization of cutting conditions and analysis of their effect on tool wear and surface roughness during hard turning of AISI-H11 steel with a coated mixed ceramic tool, J. Eng. Manuf. B 223 (2009) 144–1453 [Google Scholar]
  7. P. Thangavel, V. Selladurai, R. Shanmugam, Application of response surface methodology for predicting flank wear in turning operation, Proc. IMechE 220 (2006) 997–1003 [CrossRef] [Google Scholar]
  8. H. Öktem, T. Erzurumlu, H. Kurtaran, Application of response surface methodology in the optimization of cutting conditions for surface roughness, J. Mater. Process. Technol. 170 (2005) 11–16 [Google Scholar]
  9. A.A. Premnath, T. Alwarsamy, T. Abhinav, C.A. Krishnakant, Surface roughness prediction by response surface methodology in milling of hybrid aluminium composites, Proc. Eng. 38 (2012) 745–752 [CrossRef] [Google Scholar]
  10. A. Ebadnejad, G.R. Karimi, H. Dehghani, Application of response surface methodology for modeling of ball mills in copper sulphide ore grinding, Powder Technol. 245 (2013) 292–296 [CrossRef] [Google Scholar]
  11. M. Elbah, M.A. Yallese, H. Aouici, T. Mabrouki, J-F Rigal, Comparative assessment of wiper and conventional ceramic tools on surface roughness in hard turning AISI 4140 steel, Measurement 46 (2013) 304–3056 [Google Scholar]
  12. T. Rajmohan, K. Palanikumar, Application of the central composite design in optimization of machining parameters in drilling hybrid metal matrix composites, Measurement 46 (2013) 1470–148 [CrossRef] [Google Scholar]
  13. P.K. Kankar, S.P. Harsha, P. Kumar, Satish C. Sharma, Fault diagnosis of a rotor bearing system using response surface method, Eur. J. Mech. A/Solids 28 (2009) 841–857 [CrossRef] [Google Scholar]
  14. Z. Hessainia, A. Belbah, M.A. Yallese, T. Mabrouki, J-F. Rigal, On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations, Measurement 6 (2013) 1671–168 [CrossRef] [Google Scholar]
  15. M.W. Azizi, S. Belhadi, M.A. Yallese, T. Mabrouki, J.-F. Rigal,Surface roughness and cutting forces modeling for optimization of machining condition in finish hard turning of AISI 52100 steel, J. Mech. Sci. Technol. 26 (2012) 410–4114 [Google Scholar]
  16. E.D. Kirby, Z. Zhang, J.C. Chen, Development of an accelerometer based surface roughness prediction system in turning operation using multiple regression techniques, J. Ind. Technol. 4 (2004) 1–8 [Google Scholar]
  17. A. Doniavi, M. Eskanderzade, M. Tahmsebian, Empirical modeling of surface roughness in turning process of 1060 steel using factorial design methodology, J. Appl. Sci. 7 (2007) 2509–2513 [CrossRef] [Google Scholar]
  18. J.P. Davim, L. Figueira, Machinability evaluation in hard turning of cold work tool steel (D2) with ceramic tools using statistical techniques, Mater. Des. 28 (2007) 118–1191 [Google Scholar]
  19. X. Feng, X. Wang, Development of empirical models for surface roughness prediction in finish turning, J. Adv. Manuf. Technol. 20 (2002) 348–356 [CrossRef] [Google Scholar]
  20. I.A. Choudhury, M.A. El-Baradie, Surface roughness prediction in the turning of high-strength steel by factorial design of experiments, J. Mater. Process. Technol. 67 (1997) 55–61 [CrossRef] [Google Scholar]
  21. J.D. Thiele, S.N. Melkote, Effect of cutting edge geometry and workpiece hardness on surface generation in the finish hard turning of AISI 52100 steel, J. Mater. Process. Technol. 94 (1999) 216–226 [Google Scholar]
  22. A.I. Khuri, S. Mukhopadhyay, Response surface methodology, WIREs Comput. Stat. 2 (2010) 12–149 [Google Scholar]
  23. D.C. Montgomery, Design and Analysis of Experiments, 6th edition, John Wiley & Sons, 2004 [Google Scholar]
  24. R.H. Myers, D.C. Montgomery, in: Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 2nd edition, John Wiley & Sons, New York, 2002 [Google Scholar]
  25. H. Bouchelaghem, M.A. Yallese, A. Amirat, T. Mabrouki, J-F. Rigal, Experimental investigation and performance analyses of CBN insert in hard turning of cold work tool steel (D3), Mach. Sci. Technol. 14 (2010) 471–501 [CrossRef] [Google Scholar]
  26. H. Aouici, M.A. Yallese, K. Chaoui, T. Mabrouki, J.-F. Rigal, Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization, Measurement 45 (2012) 34–353 [Google Scholar]

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