Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm
Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processi...
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MDPI AG
2023-08-01
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| author | Manikandan Natarajan Thejasree Pasupuleti Jayant Giri Neeraj Sunheriya Lakshmi Narasimhamu Katta Rajkumar Chadge Chetan Mahatme Pallavi Giri Saurav Mallik Kanad Ray |
| author_facet | Manikandan Natarajan Thejasree Pasupuleti Jayant Giri Neeraj Sunheriya Lakshmi Narasimhamu Katta Rajkumar Chadge Chetan Mahatme Pallavi Giri Saurav Mallik Kanad Ray |
| author_sort | Manikandan Natarajan |
| collection | DOAJ |
| description | Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions. |
| first_indexed | 2024-03-10T23:51:35Z |
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| spelling | doaj.art-916550ce9aa240118f66e407164e1dde2023-11-19T01:34:41ZengMDPI AGInformation2078-24892023-08-0114843910.3390/info14080439Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning AlgorithmManikandan Natarajan0Thejasree Pasupuleti1Jayant Giri2Neeraj Sunheriya3Lakshmi Narasimhamu Katta4Rajkumar Chadge5Chetan Mahatme6Pallavi Giri7Saurav Mallik8Kanad Ray9Department of Mechanical Engineering, School of Engineering, Mohan Babu University, Tirupati 517102, IndiaDepartment of Mechanical Engineering, School of Engineering, Mohan Babu University, Tirupati 517102, IndiaDepartment of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaDepartment of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaDepartment of Mechanical Engineering, School of Engineering, Mohan Babu University, Tirupati 517102, IndiaDepartment of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaDepartment of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaLaxminarayan Intitute of Technology, Nagpur 440010, IndiaDepartment of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USAAmity School of Applied Sciences, Amity University Rajasthan, Jaipur 303002, IndiaTitanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions.https://www.mdpi.com/2078-2489/14/8/439Ti-6Al-4V (grade 5)WEDMTaguchi approachresponse analysisGRA methodartificial intelligence tools |
| spellingShingle | Manikandan Natarajan Thejasree Pasupuleti Jayant Giri Neeraj Sunheriya Lakshmi Narasimhamu Katta Rajkumar Chadge Chetan Mahatme Pallavi Giri Saurav Mallik Kanad Ray Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm Information Ti-6Al-4V (grade 5) WEDM Taguchi approach response analysis GRA method artificial intelligence tools |
| title | Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm |
| title_full | Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm |
| title_fullStr | Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm |
| title_full_unstemmed | Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm |
| title_short | Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm |
| title_sort | machinability of titanium grade 5 alloy for wire electrical discharge machining using a hybrid learning algorithm |
| topic | Ti-6Al-4V (grade 5) WEDM Taguchi approach response analysis GRA method artificial intelligence tools |
| url | https://www.mdpi.com/2078-2489/14/8/439 |
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