Abstract
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.
Highlights
Electrical discharge machining grew over the last few decades from uniqueness to a mainstream manufacturing method
The necessary parameters are chosen either through handbooks or through experience and with respect to different machining parameters followed by parametric optimization of the process
Where R2 = 100% indicates that 100% of total variation in the response that is explained by predictors or factors in the model and R2 adj is 100%, which accounts for the number of predictors in the model describes the significance of the relationship
Summary
Electrical discharge machining grew over the last few decades from uniqueness to a mainstream manufacturing method. It is most widely and successfully applied to the machining of a varied piece of work material in the advance industry [1]. The necessary parameters are chosen either through handbooks or through experience and with respect to different machining parameters followed by parametric optimization of the process. This is a tough task, statistical methods are employed to solve this problem
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