Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX

Abstract

The Altemp HX is a nickel-based superalloy having many applications in chemical, nuclear, aerospace, and marine industries. Machining such superalloys is challenging as it may cause both tool and surface damage. WEDM, a non-contact machining technique, can be employed in the machining of such alloys. In the present study, different input parameters which include pulse on time, wire span, and servo gap voltage were investigated. The cutting velocity, surface roughness, recast layer, and microhardness variations were examined on the WEDMed surface. The genetic algorithm was used to optimize the cutting velocity and surface roughness, thereby improving the overall quality of the product. The highest recast layer values were recorded as 25.8 µm, and the lowest microhardness was 170 HV. Response surface methodology and artificial neural network were employed for the prediction of cutting velocity and surface roughness. Artificial neural network prediction technique was the most efficient method for the prediction of response parameters as it predicted an error percentage lesser than 6%.