Development of neural network models for wire electrical discharge machining of Haste alloy

Abstract

Unconventional methods of material removal have been developed for fulfilling the difficulties of traditional machining and appealed to be a suitable alternate approach for traditional material removal. Wire Electrical Discharge Machining (WEDM) is one among the available advanced machining process that has been mainly adopted to machine hard materials. Proper use of intelligent decision making tools helps the manufacturer to attain benefits in manufacturing fields. Haste Alloy C276 is a nickel alloy, known as difficult to machine materials and exclusively used to various engineering files such as nuclear, gas and aerospace applications. Since it has higher strength and lower thermal conductivity, it has the tendency of reduced life of tool and poor machining performance by the use of traditional machining processes. This present investigation describes the analysis on process variables and development of neural network models for WEDM process. The experimental runs are planned and analyzed by Taguchi’s approach. Grey Relational Analysis (GRA) is adopted for attaining the Grey Relational Grade (GRG) for representing a multi-performance index. An Artificial Neural Network (ANN) model has been proposed to predict the Grey Relational Grade (GRG). Grey Relational Coefficient (GRC) values which are generated by the GRA method, given as input data for developing the Neural Network (NN) model to predict the desired multi-performance index (GRG). The closeness among the actual and predicted values are derived with the help of a comparative analysis.