PERFORMANCE STUDY OF TOOL MATERIALS AND OPTIMIZATION OF PROCESS PARAMETERS DURING EDM ON ZrB2-SiC COMPOSITE THROUGH PARTICLE SWARM OPTIMIZATION ALGORITHM

摘要

This paper deals with optimization of EDM of ZrB2-SiC composite using Particle swarm optimization (PSO). In this work ZrB2 with different volume proportions of SiC (15, 20, 25 and 30%) are selected as workpiece. ZrB2SiC ultra high temperature ceramics exhibited an excellent thermal-oxidative and configurationally stable under supersonic conditions, which suggests they are potential candidates for leading edges. Results indicate that ZrB2SiC can maintain the high oxidation resistance coupled with configurationally stable at temperatures lower than that point which results in significant softening and degradation of the oxide scale, and that point will be the temperature limit for UHTC.It is a candidate for high temperature aerospace applications such as hypersonic flight or rocket propulsion systems. To expand its area of applications, machining is mandatory. Due to high strength and hardness of ZrB2 mechanical machining is very difficult or even impossible. Electrical discharge machining is promising technology to machine ceramic components of complex shape with high-dimensional accuracy and good surface roughness. In this investigation the influence of SiC over the machinability is carried out. Input parameters are pulse on time, pulse off time and tool materials (graphite, titanium niobium, tantalum and tungsten). Pulse on time and pulse off time are kept at three different levels. Objective is to maximize the material removal rate (MRR) and to minimize the roundness, surface roughness (SR), tool wear rate (TWR), Overcut and taper angle during EDM of hot pressed ZrB2-SiC composite. In general Desirability Functional Analysis (DFA) is used to combine multiple quality characteristics into a single performance statistics. While combining the quality characteristics, weight should be assigned to each response. For this problem, unequal weights are assigned using particle swarm optimization (PSO). Interaction of pulse on time with tool material is investigated using analysis of variance (ANOVA) and it shows that tool material is most significant factor.