A novel approach of GEF and GA for the optimization of multi-objective wire EDM process during the machining of DC53 super alloy. (August 2021) - Kumar, Satish; Gupta, Arun Kumar; Chandna, Pankaj; Bhushan, Gian; Kumar, Anish
In components manufacturing, the Wire EDM is more popular due to its outstanding features of high dimensional accuracy, lower cost of production and good surface finish as there is no physical contact between the wire and work piece. However, for the steel with higher hardness, it is difficult to obtain these features up to the required extent. Moreover, with the help of optimum process parameters selection the WEDM performance characteristics should be improved. The most commonly studied responses for this process are material removal rate (MRR), Surface finish, Kerf width, wire consumption, roundness error. Among these, the responses like Kerf width, MRR and surface roughness are the primary responses. It is observed that pulse on time, pulse off time, servo voltage, peak current and wire feed are the influencing input parameters to these primary responses. Therefore, in the present work, the optimal level of input parameters is estimated for these responses using the Grey-Entropy-Fuzzy (GEF) and Genetic Algorithm (GA) during wire EDM of steel grade DC53 with high hardness of HRC58normally used in stamping dies, injection moulding and compression Moulding etc. To convert the multi objective problem into a single objective, the grey relational coefficient (GRC) has been calculated using Grey Relational Analysis and the weight-age of each response is approximated during the entropy method. To estimate the relation among the input parameters and single objective (GRC) fuzzy mathematical modelling technique termed as GEF has been applied. The optimal performance has been calculated using GA and GEF model is considered as fitness function. Five conformational tests on optimum parameter combination suggested by GA has been performed. The predicted values and experimental values have been found to be in good agreement with a standard error of 3.31% hence the prediction performance of the GREG-Fuzzy is quite satisfactory.