Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA
The manuscript discusses the abrasive water jet machining (AWJM) of Al–NiTi and Al–NiTi–nano SiC composites to understand the influences and the effect of each parameter and to indentify optimal combination of AWJM parameters. The experiments are planned and conducted based on L27 orthogonal array....
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/62323/1/Machinability%20performance%20of%20Al%E2%80%93NiTi%20and%20Al%E2%80%93NiTi%E2%80%93nano%20SiC%20composites%20with%20parametric%20optimization%20using%20GSA.pdf |
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| author | Mokkandi, Purusothaman Jayamani, Manivannan Sekarbabu, Balakiruthiha Kadambarajan, Jeya Prakash Nagarajan, Rajini Hameed Sultan, Mohamed Thariq Shanmugavel, Rajesh |
| author_facet | Mokkandi, Purusothaman Jayamani, Manivannan Sekarbabu, Balakiruthiha Kadambarajan, Jeya Prakash Nagarajan, Rajini Hameed Sultan, Mohamed Thariq Shanmugavel, Rajesh |
| author_sort | Mokkandi, Purusothaman |
| collection | UPM |
| description | The manuscript discusses the abrasive water jet machining (AWJM) of Al–NiTi and Al–NiTi–nano SiC composites to understand the influences and the effect of each parameter and to indentify optimal combination of AWJM parameters. The experiments are planned and conducted based on L27 orthogonal array. Pressure, standoff distance, and transverse feed rate are considered as input parameters; surface roughness and kerf angle are considered as output parameters. Gravitational search algorithm (GSA) is employed to identify the best possible combination of AWJM parameters. Regression model is used to develop the surface roughness and kerf angle model for Al–NiTi and Al–NiTi–nano SiC composites. The developed mathematical model is used as fitness function in GSA. It is found from the result of GSA that the optimal value for surface roughness of Al–NiTi composite is set at pressure 176 kPa, standoff distance 1 mm, and transverse feed 20 mm/min and for Al–NiTi–nano SiC composite is set at pressure 180 kPa, standoff distance 1.1 mm, and transverse feed 20 mm/min. Similarly, for kerf angle, the optimal value for Al–NiTi composite is set at pressure 260, standoff distance 1 mm, and transverse feed 20 mm/min and for Al–NiTi–nano SiC composite is set at pressure 255 kPa, standoff distance 1 mm, and transverse feed 20 mm/min. Analysis of variance is also performed to understand the effect of each input parameter on output response. |
| first_indexed | 2024-03-06T09:42:35Z |
| format | Article |
| id | upm.eprints-62323 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:42:35Z |
| publishDate | 2017 |
| publisher | Springer |
| record_format | dspace |
| spelling | upm.eprints-623232019-10-31T04:29:01Z http://psasir.upm.edu.my/id/eprint/62323/ Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA Mokkandi, Purusothaman Jayamani, Manivannan Sekarbabu, Balakiruthiha Kadambarajan, Jeya Prakash Nagarajan, Rajini Hameed Sultan, Mohamed Thariq Shanmugavel, Rajesh The manuscript discusses the abrasive water jet machining (AWJM) of Al–NiTi and Al–NiTi–nano SiC composites to understand the influences and the effect of each parameter and to indentify optimal combination of AWJM parameters. The experiments are planned and conducted based on L27 orthogonal array. Pressure, standoff distance, and transverse feed rate are considered as input parameters; surface roughness and kerf angle are considered as output parameters. Gravitational search algorithm (GSA) is employed to identify the best possible combination of AWJM parameters. Regression model is used to develop the surface roughness and kerf angle model for Al–NiTi and Al–NiTi–nano SiC composites. The developed mathematical model is used as fitness function in GSA. It is found from the result of GSA that the optimal value for surface roughness of Al–NiTi composite is set at pressure 176 kPa, standoff distance 1 mm, and transverse feed 20 mm/min and for Al–NiTi–nano SiC composite is set at pressure 180 kPa, standoff distance 1.1 mm, and transverse feed 20 mm/min. Similarly, for kerf angle, the optimal value for Al–NiTi composite is set at pressure 260, standoff distance 1 mm, and transverse feed 20 mm/min and for Al–NiTi–nano SiC composite is set at pressure 255 kPa, standoff distance 1 mm, and transverse feed 20 mm/min. Analysis of variance is also performed to understand the effect of each input parameter on output response. Springer 2017-10 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/62323/1/Machinability%20performance%20of%20Al%E2%80%93NiTi%20and%20Al%E2%80%93NiTi%E2%80%93nano%20SiC%20composites%20with%20parametric%20optimization%20using%20GSA.pdf Mokkandi, Purusothaman and Jayamani, Manivannan and Sekarbabu, Balakiruthiha and Kadambarajan, Jeya Prakash and Nagarajan, Rajini and Hameed Sultan, Mohamed Thariq and Shanmugavel, Rajesh (2017) Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA. Journal of the Australian Ceramic Society, 53 (2). 599 - 609. ISSN 2510-1560; ESSN: 2510-1579 https://link.springer.com/article/10.1007/s41779-017-0072-4 10.1007/s41779-017-0072-4 |
| spellingShingle | Mokkandi, Purusothaman Jayamani, Manivannan Sekarbabu, Balakiruthiha Kadambarajan, Jeya Prakash Nagarajan, Rajini Hameed Sultan, Mohamed Thariq Shanmugavel, Rajesh Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA |
| title | Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA |
| title_full | Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA |
| title_fullStr | Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA |
| title_full_unstemmed | Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA |
| title_short | Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA |
| title_sort | machinability performance of al niti and al niti nano sic composites with parametric optimization using gsa |
| url | http://psasir.upm.edu.my/id/eprint/62323/1/Machinability%20performance%20of%20Al%E2%80%93NiTi%20and%20Al%E2%80%93NiTi%E2%80%93nano%20SiC%20composites%20with%20parametric%20optimization%20using%20GSA.pdf |
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