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Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX
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...
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Published in: | Advances in materials science and engineering 2022, Vol.2022, p.1-9 |
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description | 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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V. ; Soni, Hargovind ; Narendranath, S. ; Mashinini, P. M. ; Kara, Fuat</creator><contributor>Ares, Alicia E. ; Alicia E Ares</contributor><creatorcontrib>Manoj, I. V. ; Soni, Hargovind ; Narendranath, S. ; Mashinini, P. M. ; Kara, Fuat ; Ares, Alicia E. ; Alicia E Ares</creatorcontrib><description>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%.</description><identifier>ISSN: 1687-8434</identifier><identifier>EISSN: 1687-8442</identifier><identifier>DOI: 10.1155/2022/5192981</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Aerospace industry ; Alloys ; Artificial neural networks ; Dimensional analysis ; Electric discharge machining ; Genetic algorithms ; Investigations ; Microhardness ; Nickel base alloys ; Optimization ; Predictions ; Process parameters ; Response surface methodology ; Superalloys ; Surface roughness ; Variance analysis ; Velocity</subject><ispartof>Advances in materials science and engineering, 2022, Vol.2022, p.1-9</ispartof><rights>Copyright © 2022 I. V. Manoj et al.</rights><rights>Copyright © 2022 I. V. Manoj et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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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. 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subjects | Aerospace industry Alloys Artificial neural networks Dimensional analysis Electric discharge machining Genetic algorithms Investigations Microhardness Nickel base alloys Optimization Predictions Process parameters Response surface methodology Superalloys Surface roughness Variance analysis Velocity |
title | Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX |
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