article · Advances in Materials Science and Engineering
Altemp HX is a nickel-based superalloy used across the chemical, nuclear, aerospace, and marine industries, but conventional machining risks damaging both cutting tools and material surfaces. Wire electrical discharge machining provides a non-contact alternative for processing this resilient material. Experimental work investigated how operational parameters, specifically pulse on time, wire span, and servo gap voltage, affected cutting velocity, surface roughness, recast layer formation, and microhardness. A genetic algorithm was implemented to optimise the process by balancing cutting velocity against surface roughness to enhance product quality. To forecast operational outcomes, researchers compared response surface methodology with artificial neural networks. The artificial neural network proved to be the superior predictive tool, forecasting cutting velocity and surface roughness with an error rate under 6 percent.
Superalloys are vital for harsh environments in aerospace and power generation, but their extreme durability makes them difficult to cut without degrading components or wearing out machinery. Demonstrating that non-contact electrical discharge machining can be accurately modelled and optimised helps manufacturers maintain surface integrity, minimise structural defects, and establish reliable machining standards for difficult-to-cut metals.
This applied research offers direct relevance to precision tooling and manufacturing facilities serving aerospace, marine, nuclear, and chemical sectors. The predictive artificial neural network model could be adapted by computer-aided manufacturing software developers or machining operators to automate parameter selection for Altemp HX components. Currently at an experimental testing stage, the technique requires integration and validation within industrial production workflows before full commercial adoption.
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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%.
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DOI: 10.1155/2022/5192981
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