Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. Selection of cutting parameters for obtaining higher cutting efficiency or accuracy in WEDM is still not fully solved, even with most up-to-date CNC WEDM machine. It is widely recognised that Acoustic Emission (AE) is gaining ground as a monitoring method for health diagnosis on rotating machinery. The advantage of AE monitoring over vibration monitoring is that the AE monitoring can detect the growth of subsurface cracks whereas the vibration monitoring can detect defects only when they appear on the surface. This study outlines the estimation of AE parameters viz., signal strength, absolute energy, RMS in the WEDM. Stavax (modified AISI 420) steel material was machined using different process parameters based on Taguchi’s L’16 standard orthogonal array. Among different process parameters voltage and flush rate were kept constant. Parameters such as pulse-on time, pulse-off time, current and bed speed was varied. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Simple functional relationships between the parameters were plotted to arrive at possible information on surface roughness and AE signals. But these simpler methods of analysis did not provide any information about the status of the work material. Thus, there is a requirement for more sophisticated methods that are capable of integrating information from the multiple sensors. Hence, methods like Multiple Regression Analysis (MRA) and Group Method of Data Handling (GMDH) have been applied for the estimation of surface roughness, AE signal strength, AE absolute energy and AE RMS. The GMDH algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into two sets: the training set and testing set. The training set is used to make the GMDH learn the process and the testing set will check the performance of GMDH. Different models can be obtained by varying the percentage of data in the training set and the best model can be selected from these, viz., 50%, 62.5% and 75%. The best model is selected from the said percentages of data. Number of variables selected at each layer is usually taken as a fixed number or a constantly increasing number. It is usually given as fractional increase in number of independent variables present in the previous level. Three different criterion functions, viz., Root Mean Square (Regularity) criterion, Unbiased criterion and Combined criterion were considered for the estimation. The choice of criterion for node selection is another important parameter for proper modeling. From the results it was observed that, AE parameters and estimated surface roughness values were correlates well with GMDH when compare to MRA.

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