Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach

Author:

Tzeng Chorng-Jyh,Chen Rui-Yang

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering

Reference21 articles.

1. Debabrata, M., Surjya, K. P., and Partha, S., “Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II,” Journal of Materials Processing Technology, Vol. 186, No. 1–3, pp. 154–162, 2007.

2. Bhattacharyya, B., Gangopadhyay, S., and Sarkar, B. R., “Modelling and analysis of EDM job surface integrity,” Journal of Materials Processing Technology, Vol. 189, No. 1–3, pp. 169–177, 2007.

3. Zarepour, H., Fadaei Tehrani, A., Karimi, D., and Amini, S., “Statisticalanalysis on electrode wear in EDM of tool steel DIN1.2714 used in forging dies,” Journal of Materials Processing Technology, Vol. 187–188, pp. 711–714, 2007.

4. Kao, J. Y., Tsao, C. C., Wang, S. S., and Hsu, C. Y., “Optimization of the EDM parameters on machining Ti-6Al-4V with multiple quality characteristics,” The International Journal of Advanced Manufacturing Technology, Vol. 47, No. 1–4, pp. 395–402, 2010.

5. Horacio, T., Sánchez, M., and Félix, E., “Development of an inversion model for establishing EDM input parameters to satisfy material removal rate, electrode wear ratio and surface roughness,” The International Journal of Advanced Manufacturing Technology, Vol. 57, No. 1–4, pp. 189–201, 2011.

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