Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy
Aluminium alloys are gaining popularity in a diversity of engineering applications because of their extraordinary features such as strength, resistance to oxidation, and so on. AA5052 (Al-Mg series) is generally used in antirust uses, particularly in desalination related activities, due to its bette...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/1476770 |
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| author | Katta Lakshmi Narasimhamu Manikandan Natarajan Pasupuleti Thejasree Emad Makki Jayant Giri Neeraj Sunheriya Rajkumar Chadge Chetan Mahatme Pallavi Giri T. Sathish |
| author_facet | Katta Lakshmi Narasimhamu Manikandan Natarajan Pasupuleti Thejasree Emad Makki Jayant Giri Neeraj Sunheriya Rajkumar Chadge Chetan Mahatme Pallavi Giri T. Sathish |
| author_sort | Katta Lakshmi Narasimhamu |
| collection | DOAJ |
| description | Aluminium alloys are gaining popularity in a diversity of engineering applications because of their extraordinary features such as strength, resistance to oxidation, and so on. AA5052 (Al-Mg series) is generally used in antirust uses, particularly in desalination related activities, due to its better resistance to corrosion in marine applications at temperature ranges up to 125°C, lower cost, better heat-carrying capacity, and nontoxicity of its corrosion components. Drilling is one of the most commonly adopted material removal processes that is adopted in numerous engineering uses. Taguchi’s technique is engaged to arrange and examine the tests, by treating the drilling diameter and speed and as independent process factors. The studies were carried out using an L27 orthogonal array. Material removal rate (MRR), surface roughness (SR), and form/orientation error are deemed as output characteristics. Taguchi’s analysis was engaged to discover the best process factors. ANOVA is used to examine the influence of process variables. Suitable application of artificial intelligence tools for making effective decision assists the manufacturer in accomplishing the benefits in numerous engineering domains. To obtain the maximum material removal and minimum roughness, circularity (circ), and perpendicularity errors (perp), the process variables have been optimized with the help of grey-ANFIS-amalgamated with Jaya algorithm. The multiperformance index was developed using grey theory. Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best-suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics. |
| format | Article |
| id | doaj-art-0846ac0f89a34afb8c19f24836f9d3cb |
| institution | Kabale University |
| issn | 2314-4912 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-0846ac0f89a34afb8c19f24836f9d3cb2025-08-20T03:55:32ZengWileyJournal of Engineering2314-49122024-01-01202410.1155/2024/1476770Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium AlloyKatta Lakshmi Narasimhamu0Manikandan Natarajan1Pasupuleti Thejasree2Emad Makki3Jayant Giri4Neeraj Sunheriya5Rajkumar Chadge6Chetan Mahatme7Pallavi Giri8T. Sathish9Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringLaxminarayan Institute of TechnologyDepartment of Mechanical EngineeringAluminium alloys are gaining popularity in a diversity of engineering applications because of their extraordinary features such as strength, resistance to oxidation, and so on. AA5052 (Al-Mg series) is generally used in antirust uses, particularly in desalination related activities, due to its better resistance to corrosion in marine applications at temperature ranges up to 125°C, lower cost, better heat-carrying capacity, and nontoxicity of its corrosion components. Drilling is one of the most commonly adopted material removal processes that is adopted in numerous engineering uses. Taguchi’s technique is engaged to arrange and examine the tests, by treating the drilling diameter and speed and as independent process factors. The studies were carried out using an L27 orthogonal array. Material removal rate (MRR), surface roughness (SR), and form/orientation error are deemed as output characteristics. Taguchi’s analysis was engaged to discover the best process factors. ANOVA is used to examine the influence of process variables. Suitable application of artificial intelligence tools for making effective decision assists the manufacturer in accomplishing the benefits in numerous engineering domains. To obtain the maximum material removal and minimum roughness, circularity (circ), and perpendicularity errors (perp), the process variables have been optimized with the help of grey-ANFIS-amalgamated with Jaya algorithm. The multiperformance index was developed using grey theory. Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best-suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics.http://dx.doi.org/10.1155/2024/1476770 |
| spellingShingle | Katta Lakshmi Narasimhamu Manikandan Natarajan Pasupuleti Thejasree Emad Makki Jayant Giri Neeraj Sunheriya Rajkumar Chadge Chetan Mahatme Pallavi Giri T. Sathish Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy Journal of Engineering |
| title | Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy |
| title_full | Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy |
| title_fullStr | Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy |
| title_full_unstemmed | Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy |
| title_short | Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy |
| title_sort | development of hybrid optimization model using grey anfis jaya algorithm for cnc drilling of aluminium alloy |
| url | http://dx.doi.org/10.1155/2024/1476770 |
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