Experimental Investigations of Using Aluminum Oxide (Al<sub>2</sub>O<sub>3</sub>) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy
In the present study, a comprehensive parametric analysis was carried out using the electrical discharge machining of Ti6Al4V, using pulse-on time, current, and pulse-off time as input factors with output measures of surface roughness and material removal rate. The present study also used two differ...
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MDPI AG
2023-12-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/14/12/2247 |
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| author | Rakesh Chaudhari Sakshum Khanna Vivek K. Patel Jay Vora Soraya Plaza Luis Norberto López de Lacalle |
| author_facet | Rakesh Chaudhari Sakshum Khanna Vivek K. Patel Jay Vora Soraya Plaza Luis Norberto López de Lacalle |
| author_sort | Rakesh Chaudhari |
| collection | DOAJ |
| description | In the present study, a comprehensive parametric analysis was carried out using the electrical discharge machining of Ti6Al4V, using pulse-on time, current, and pulse-off time as input factors with output measures of surface roughness and material removal rate. The present study also used two different nanopowders, namely alumina and nano-graphene, to analyze their effect on output measures and surface defects. All the experimental runs were performed using Taguchi’s array at three levels. Analysis of variance was employed to study the statistical significance. Empirical relations were generated through Minitab. The regression model term was observed to be significant for both the output responses, which suggested that the generated regressions were adequate. Among the input factors, pulse-off time and current were found to have a vital role in the change in material removal rate, while pulse-on time was observed as a vital input parameter. For surface quality, pulse-on time and pulse-off time were recognized to be influential parameters, while current was observed to be an insignificant factor. Teaching–learning-based optimization was used for the optimization of output responses. The influence of alumina and nano-graphene powder was investigated at optimal process parameters. The machining performance was significantly improved by using both powder-mixed electrical discharge machining as compared to the conventional method. Due to the higher conductivity of nano-graphene powder, it showed a larger improvement as compared to alumina powder. Lastly, scanning electron microscopy was operated to investigate the impact of alumina and graphene powder on surface morphology. The machined surface obtained for the conventional process depicted more surface defects than the powder-mixed process, which is key in aeronautical applications. |
| first_indexed | 2024-03-08T20:32:15Z |
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| id | doaj.art-bee7c914524a49d79e0652d44cd8f7d7 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-08T20:32:15Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
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| series | Micromachines |
| spelling | doaj.art-bee7c914524a49d79e0652d44cd8f7d72023-12-22T14:25:32ZengMDPI AGMicromachines2072-666X2023-12-011412224710.3390/mi14122247Experimental Investigations of Using Aluminum Oxide (Al<sub>2</sub>O<sub>3</sub>) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium AlloyRakesh Chaudhari0Sakshum Khanna1Vivek K. Patel2Jay Vora3Soraya Plaza4Luis Norberto López de Lacalle5Department of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, IndiaSchool of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, IndiaDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, IndiaDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, IndiaDepartment of Mechanical Engineering, University of the Basque Country, Escuela Superior de Ingenieros Alameda de Urquijo s/n, 48013 Bilbao, SpainDepartment of Mechanical Engineering, University of the Basque Country, Escuela Superior de Ingenieros Alameda de Urquijo s/n, 48013 Bilbao, SpainIn the present study, a comprehensive parametric analysis was carried out using the electrical discharge machining of Ti6Al4V, using pulse-on time, current, and pulse-off time as input factors with output measures of surface roughness and material removal rate. The present study also used two different nanopowders, namely alumina and nano-graphene, to analyze their effect on output measures and surface defects. All the experimental runs were performed using Taguchi’s array at three levels. Analysis of variance was employed to study the statistical significance. Empirical relations were generated through Minitab. The regression model term was observed to be significant for both the output responses, which suggested that the generated regressions were adequate. Among the input factors, pulse-off time and current were found to have a vital role in the change in material removal rate, while pulse-on time was observed as a vital input parameter. For surface quality, pulse-on time and pulse-off time were recognized to be influential parameters, while current was observed to be an insignificant factor. Teaching–learning-based optimization was used for the optimization of output responses. The influence of alumina and nano-graphene powder was investigated at optimal process parameters. The machining performance was significantly improved by using both powder-mixed electrical discharge machining as compared to the conventional method. Due to the higher conductivity of nano-graphene powder, it showed a larger improvement as compared to alumina powder. Lastly, scanning electron microscopy was operated to investigate the impact of alumina and graphene powder on surface morphology. The machined surface obtained for the conventional process depicted more surface defects than the powder-mixed process, which is key in aeronautical applications.https://www.mdpi.com/2072-666X/14/12/2247EDMTi6Al4VTLBO algorithmaluminum oxide (Al<sub>2</sub>O<sub>3</sub>) nanopowdernano-graphene |
| spellingShingle | Rakesh Chaudhari Sakshum Khanna Vivek K. Patel Jay Vora Soraya Plaza Luis Norberto López de Lacalle Experimental Investigations of Using Aluminum Oxide (Al<sub>2</sub>O<sub>3</sub>) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy Micromachines EDM Ti6Al4V TLBO algorithm aluminum oxide (Al<sub>2</sub>O<sub>3</sub>) nanopowder nano-graphene |
| title | Experimental Investigations of Using Aluminum Oxide (Al<sub>2</sub>O<sub>3</sub>) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy |
| title_full | Experimental Investigations of Using Aluminum Oxide (Al<sub>2</sub>O<sub>3</sub>) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy |
| title_fullStr | Experimental Investigations of Using Aluminum Oxide (Al<sub>2</sub>O<sub>3</sub>) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy |
| title_full_unstemmed | Experimental Investigations of Using Aluminum Oxide (Al<sub>2</sub>O<sub>3</sub>) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy |
| title_short | Experimental Investigations of Using Aluminum Oxide (Al<sub>2</sub>O<sub>3</sub>) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy |
| title_sort | experimental investigations of using aluminum oxide al sub 2 sub o sub 3 sub and nano graphene powder in the electrical discharge machining of titanium alloy |
| topic | EDM Ti6Al4V TLBO algorithm aluminum oxide (Al<sub>2</sub>O<sub>3</sub>) nanopowder nano-graphene |
| url | https://www.mdpi.com/2072-666X/14/12/2247 |
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