Modeling and optimization of machining parameters are very important in any machining processes. The current study provides predictive models for the functional relationship between various factors and responses of electrical discharge machined AISI D2 steel component. Surface Roughness (Ra) is important as it influences the quality and performance of the products, hence the minimization of surface roughness in manufacturing sectors is of maximum importance. It is also realistic and desirable if the finished parts do not need further any operations to meet the required optimum level of surface quality. For achieving the required optimum levels of surface quality, the proper selection of machining parameters in EDM is essential. Four significant machining parameters, Ip (Pulse Current), Ton (Pulse on Time), Toff (Off Time) and V (Gap Voltage) in the EDM process have been selected and with the various combination experiments were conducted. A mathematical regression model was developed to predict the average Surface Roughness in electrical discharge machined surface. The developed model was validated with new experimental data. The model was coupled with genetic algorithm to predict the minimum possible surface roughness. It is found that the predicted and experimental values were close to a certain extent, which specifies that the established model can be successfully used to predict the surface roughness. Also, the developed model could be used for the selection of the levels in the EDM process for saving in machining time and product cost can be achieved by utilizing the model.

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Domination in graphs has been extensively studied and adopted in many real life applications. The monitoring electrical power system is a variant of a domination problem called power domination problem. Another variant is the zero forcing problem. Determining minimum cardinality of a power dominating set and zero forcing set in a graph are the power domination problem and zero forcing problem, respectively. Both problems are NP-complete. In this paper, we compute the power domination number and the zero forcing number for fully connected cubic networks.

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