Machines. Technologies. Materials.
Vol. 13 (2019), Issue 7, pg(s) 298-301

TECHNOLOGIES

Investigation and analysis of electrochemical machining of 321-Stainless Steel based on response surface methodology

  • 1 Department of Mechanical Engineering – University of Shahreza, Isfahan, Iran
  • 2 Faculty of Mechanical Engineering – University of Guilan, Rasht, Iran

Abstract

Electrochemical machining (ECM) is prevalent and competitive manufacturing process which uses for machining of hard and tough materials in high tech industries. Hence, experimental investigations on ECM of different materials play essential role to effectively utilize this process. This paper demonstrates a systematic approach for achieving comprehensive mathematical models in order to investigate the effect of machining parameters on the process responses of 321-Stainless-Steel and analysis of machining performance based on the response surface methodology (RSM). Machining voltage, tool feed rate, electrolyte flow rate and concentration of NaNO3 solution were considered as the machining parameters while material removal rate (MRR) and surface roughness (Ra) were considered as the process responses. Experimental plan was performed by a central composite design (CCD), and the proposed mathematical models statistically have been evaluated by analysis of variance (ANOVA). Analysis shows that the RSM method has been appointed properly as the design of experiments (DOE) method for resolving curvature in ECM process responses. Also, the results show that the machining performance is greatly influenced by machining parameters. Especially the voltage and electrolyte concentration are the most important parameters.

Keywords

References

  1. Rumyantsev, E. & Davydov, A. (1989). Electrochemical machining of metals, MIR Publishers, Moscow, Russia. Int J Adv Manuf Technol, Vol. 69, pp. 563–581.
  2. Neto, J.C.S., Silva, E.M. & Silva M.B. (2006). Intervening variables in electrochemical machining. J Mater Process Technol, Vol. 179, pp. 92–96.
  3. Montgomery D.C. (2009). Design and analysis of experiments. New York: John Wiley.
  4. Singh, G.K., Yadava, V. & Kumar, R. (2010). Multiresponse Optimization of Electro-Discharge Diamond Face Grinding Process Using Robust Design of Experiments. Mater Manuf Processes, Vol. 25(8), pp. 851-856.
  5. Moradi, M., Ghoreishi, M., Frostevarg, J. & Kaplan, A.F.H. (2013). An investigation on stability of laser hybrid arc welding. Optics and Lasers in Engineering, Vol. 51, pp. 481–487.
  6. Haridy, S., Gouda, S.A. & Wu, Z. (2011). An integrated framework of statistical process control and design of experiments for optimizing wire electrochemical turning process. Int J Adv Manuf Technol, Vol. 53, pp. 191–207.
  7. Hwang Y. K. & Lee C. M., (2010). Surface roughness and cutting force prediction in MQL and wet turning process of AISI 1045 using design of experiments. J. Mech. Sci. Technol. Vol. 24, pp. 1669-1677.
  8. Venkata Rao, R. & Kalyankar, V.D. (2014). Optimization of modern machining processes using advanced optimization techniques: a review. Int J Adv Manuf Technol, Vol. 73, pp. 1159- 1188.
  9. El-Taweel & T. A. (2008). Modelling and analysis of hybrid electrochemical turning-magnetic abrasive finishing of 6061 Al/Al2O3 composite. Int J Adv Manuf Technol, Vol. 37, pp. 705– 714.
  10. Myers, R.H. & Montgomery D.C. (1995). Response surface methodology: process and product optimization using designed experiments. New York: Wiley.

Article full text

Download PDF