Manufacturing Technology Today

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Optimization Study of Process Parameters using Genetic Algorithm in EDM


Affiliations
1 Department of Mechanical Engineering, Vignan Institute of Technology & Science, Hyderabad, India
     

   Subscribe/Renew Journal


Productivity and quality in production/manufacturing have great concerns in competitive global market; manufacturing units mainly focuses on these in relation to the process and product developed subsequently. Electrical Discharge Machining process, even now it is an experience process, wherein still the selected parameters are often far from the maximum, and at the same time selecting optimization parameters is costly and time-consuming affair. Material Removal Rate during the process has been considered in this work as a productivity estimate with the objective to maximize it, also have better surface roughness, taken as important output parameter, in the process. These two opposite objectives have been simultaneously satisfied by selecting an optimal process environment, optimal parameter setting. In this work, objective function is obtained using Regression Analysis and tested for optimization using Genetic Algorithm technique. The model is shown to be effective; MRR and Surface Roughness shown improved when used optimized machining parameters.

Keywords

EDM, Optimization, Process Parameters, Genetic Algorithm.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Bhattacharyya, B., Gangopadhyay, S., & Sarkar, B.R. (2007). Modelling and analysis of EDMED job surface integrity. Journal of Materials Processing Technology, 189(1-3), 169–177.

  • Dewangan, S., Datta, S., Patel, S.K., and Mahapatra, S.S. (2011, July 13-16th). A case study on quality and productivity optimization in electric discharge machining. 14th International Conference in Advanced Materials & Processing Technologies AMPT2011, Istanbul, Turkey.

  • Dhanabalan, S., Sivakumar, K., & Satyanarayanan, C. (2011). Optimization of EDM parameters with multiple Performance characteristics for Titanium grades. European Journal of Scientific Research, 68 (3), 297-305.

  • Joshi, S.N., & Pande, S.S. (2011). Intelligent process modeling & optimization of die-sinking electric discharge machining. Applied Soft Computing, 11(2), 2743–2755.

  • Karthikeyan, R., Lakshmi Narayanan, P.R., and Naagarazan, R.S. (1999). Mathematical modelling for electric discharge machining of aluminium-silicon carbide particulate composites. Journal of Materials Processing Technology, 87(1-3), 59-63.

  • Rao, G.K.M., Rangajanardhaa, G., Rao, D.H., and Rao, M.S. (2009). Development of Hybrid Model and Optimization of Surface Roughness in Electric Discharge Machining Using Artificial Neural Networks & Genetic Algorithm. Journal of Materials Processing Technology, 209(3), 1512-1520.

  • Saha, S.K., and Choudhury, S.K. (2009). Experimental investigation & empirical modeling of the dry electric discharge machining process. International Journal of Machine Tools and Manufacture, 49(3-4), 297-308.

  • Tzeng, C.J., & Chen, R.Y. (2013). Optimization of electric discharge machining process using the response surface methodology & genetic algorithm approach. International Journal of Precision Engineering and Manufacturing, 14, 709-717.

  • Rao, V.R. (2011). Advanced modeling & optimization of manufacturing processes. Springer Verlag Limited.

  • Wang, K., Gelgele, H.L., Wang, Y., Yuan, Q., and Fang, M. (2003). A Hybrid Intelligent Method for Modeling the EDM Process. International Journal of Machine Tools and Manufacture, 43(10), 995-999.


Abstract Views: 308

PDF Views: 0




  • Optimization Study of Process Parameters using Genetic Algorithm in EDM

Abstract Views: 308  |  PDF Views: 0

Authors

S. Deva Prasad
Department of Mechanical Engineering, Vignan Institute of Technology & Science, Hyderabad, India
K. Chandra Shekar
Department of Mechanical Engineering, Vignan Institute of Technology & Science, Hyderabad, India
B. Singaravel
Department of Mechanical Engineering, Vignan Institute of Technology & Science, Hyderabad, India
N. Venkateshwarlu
Department of Mechanical Engineering, Vignan Institute of Technology & Science, Hyderabad, India
E. Sai Santosh
Department of Mechanical Engineering, Vignan Institute of Technology & Science, Hyderabad, India

Abstract


Productivity and quality in production/manufacturing have great concerns in competitive global market; manufacturing units mainly focuses on these in relation to the process and product developed subsequently. Electrical Discharge Machining process, even now it is an experience process, wherein still the selected parameters are often far from the maximum, and at the same time selecting optimization parameters is costly and time-consuming affair. Material Removal Rate during the process has been considered in this work as a productivity estimate with the objective to maximize it, also have better surface roughness, taken as important output parameter, in the process. These two opposite objectives have been simultaneously satisfied by selecting an optimal process environment, optimal parameter setting. In this work, objective function is obtained using Regression Analysis and tested for optimization using Genetic Algorithm technique. The model is shown to be effective; MRR and Surface Roughness shown improved when used optimized machining parameters.

Keywords


EDM, Optimization, Process Parameters, Genetic Algorithm.

References