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%.
Highlights
Altemp HX is a high-temperature and high-strength superalloy having many applications
Singh and Pradhan [10] explored the parametric effects by using the Taguchi technique and Response Surface Methodology (RSM) in WEDM of AISI D2 Steel. e parameters such as material removal rate and the surface roughness were minimized in optimal settings of machining parameters
It can be observed that at the highest machining parameters, the cutting velocity was 2.455 mm/min and the surface roughness was 3.76 μm, whereas at the lowest machining parameters, the cutting velocity was 0.504 mm/min with 1.33 μm surface roughness. e surface roughness was calculated as an average of 5 values, and the cutting velocity was the average of all the machining speeds recorded at every instant of machining the material
Summary
Altemp HX is a high-temperature and high-strength superalloy having many applications. Bose and Nandi [12] have investigated and optimized material removal rate (MRR), surface roughness (SR), kerf width, and overcut using RSM. Varun and Venkaiah [13] have proposed grey relational analysis with a genetic algorithm to simultaneously optimize the response parameters such as material removal rate, surface roughness, and cutting width (kerf ). Soni et al [18] used the response surface method and ANN for the prediction of cutting speed in WEDM of shape memory alloys. Altemp HX, a nickel-based superalloy, was machined in WEDM Different input parameters such as pulse on time, wire span, and servo gap voltage on cutting velocity, surface roughness, recast layer, and microhardness were examined on the machined surface
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