Experimental Study of Surface Roughness in Wedm Process and Ann Modelling
Piyush Pant1, Navneet K Pandey2, S. Rajesha3, Gaurav Jain4
1Piyush Pant, M. Tech Research Scholar, JSSATE Noida, India.
2Navneet K Pandey, Assistant Professor ME Deptt. JSSATE, Noida, India.
3S. Rajesha, Professor ME Deptt. JSSATE. Noida, India.
4Gaurav Jain, Assistant Professor ME Deptt. JSSATE, Noida, India.
Manuscript received on May 26, 2014. | Revised Manuscript received on June 11, 2014. | Manuscript published on June 30, 2014. | PP: 57-61 | Volume-3, Issue-5, June 2014. | Retrieval Number: E3100063514/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Surface roughness is an effective parameter in representing the quality of machined surface and is one of the most common performance measurements in machining process. This paper reports the effect and optimization of pulse-on time, gap voltage, wire feed rate on surface roughness in wire electrical discharge machining (WEDM) process for die steel D3 using L27 orthogonal array. Signal-to noise (S/N) ratio and ANOVA are used as statistical analyses to achieve optimum levels and to study the population distribution of the response characteristic respectively. It has been found that pulse on-time is the most significant factor affecting the surface roughness. The experimental data is later used to model the surface roughness using artificial neural network.
Keywords: Surface roughness, Taguchi, Wire cut electrical discharge machining, Die steel Artificial neural networks.