Multi Response Optimization of WEDM Process on OHNS Die Steel Using ANN, SA and GA

Multi Response Optimization of WEDM Process on OHNS Die Steel Using ANN, SA and GA

Goutam Kumar Bose (Haldia Institute of Technology, West Bengal University of Technology, Haldia, India) and Pritam Pain (Haldia Institute of Technology, West Bengal University of Technology, Haldia, India)
DOI: 10.4018/IJMFMP.2016070102


In the present research work Oil Hardened Naturally Shrinking (OHNS) work-material which is commonly used in plastic industries is considered for machining by WEDM process. Four different control parameters are deliberated to study the effect on the responses like material removal rate, overcut and surface roughness. To reduce the total number of experiment, L27 orthogonal array is used. Analysis of Variance is applied to attain the significant process parameters affecting the responses. The effect of the responses with the control parameters is plotted through S/N ratio graphs. To find the effect of the parameters on the responses and thereby developing a mathematical model regression analysis is done. The response data are examined using artificial neural network. Single objective parametric combination for each response is obtained using simulated annealing. A multi response optimization for the responses is done initially by using genetic algorithm and finally by applying Grey relational analysis.
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1. Introduction

Wire Electrical Discharge Machining (WEDM) is a non-conventional, thermo-electric machining process which has an important role in high-precision and high-performance manufacturing industries due to it is capability of accurate and efficient machining of parts with varying hardness or complex shapes and sharp edges of the work piece (El-Hofy, 2005). In WEMD material removal take place by controlled erosion of the electrically conducting material (Mishra, 1997). The process is based on removing the material from the work piece by a series of discrete sparks between an anode work piece which is immersed in a dielectric liquid medium and continuously feeding cathode wire through the work piece control by a microprocessor (Ghosh and Mallik, 1991). This electric discharge melts and vaporizes small amounts of the work material, and the removed material is flushed away by the flowing dielectric liquid. As the tool wire and the work piece does not make direct contact during machining, so there is no machining stress, chatter and vibration. Therefore, this process can be used in machining of alloys having low machinability and high temperature strength resistance.

In order to achieve high quality, high process safety, minimal manufacturing cost and lowest possible machining time the manufacturing process parameters have to choose in the optimized way. The major factors influencing WEDM process are Pulse On Time (Ton), Pulse Off Time (Toff), Wire Feed, Wire Tension, Servo Voltage, Open Voltage, Peak Current and Dielectric Pressure (Ikram, et al. 2013). The photograph of WEDM setup is illustrated in Figure 1 below.

Figure 1.

Machining setup of WEDM

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