Study on Ductility of Ti Aluminide Using Artificial Neural Network
Improvement of ductility at room temperature has been a major concern on processing and application of Ti aluminides over the years. Modifications in alloy chemistry of binary alloy (Ti48 Al) and processing conditions were suggested through experimental studies with limited success. Using the repor...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2011/874375 |
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| author | R. K. Gupta Rama Mehta Vijaya Agarwala Bhanu Pant P. P. Sinha |
| author_facet | R. K. Gupta Rama Mehta Vijaya Agarwala Bhanu Pant P. P. Sinha |
| author_sort | R. K. Gupta |
| collection | DOAJ |
| description | Improvement of ductility at room temperature has been a major concern on processing and application of Ti aluminides over the years. Modifications in alloy chemistry of binary alloy (Ti48 Al) and processing conditions were suggested through experimental studies with limited success. Using the reported data, the present paper aims to optimize the experimental conditions through computational modeling using artificial neural network (ANN). Ductility database were prepared, and three parameters, namely, alloy type, grain size, and heat treatment cycle were selected for modeling. Additionally, ductility data were generated from the literature for training and validation of models on the basis of linearity and considering the primary effect of these three parameters. Model was trained and tested for three different datasets drawn from the generated data. Possibility of improving ductility by more than 5% is observed for multicomponent alloy with grain size of 10–50 μm following a multistep heat treatment cycle. |
| format | Article |
| id | doaj-art-d8f01db398a24df8819fed01938aca71 |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-d8f01db398a24df8819fed01938aca712025-02-03T05:59:17ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742011-01-01201110.1155/2011/874375874375Study on Ductility of Ti Aluminide Using Artificial Neural NetworkR. K. Gupta0Rama Mehta1Vijaya Agarwala2Bhanu Pant3P. P. Sinha4Materials and Mechanical Entity, Vikram Sarabhai Space Center, Trivandrum 695022, IndiaNational Institute of Hydrology, Roorkee 247667, IndiaDepartement of Metallurgical and Materials Engineering, Indian Institute of Technology, Roorkee 247667, IndiaMaterials and Mechanical Entity, Vikram Sarabhai Space Center, Trivandrum 695022, IndiaMaterials and Mechanical Entity, Vikram Sarabhai Space Center, Trivandrum 695022, IndiaImprovement of ductility at room temperature has been a major concern on processing and application of Ti aluminides over the years. Modifications in alloy chemistry of binary alloy (Ti48 Al) and processing conditions were suggested through experimental studies with limited success. Using the reported data, the present paper aims to optimize the experimental conditions through computational modeling using artificial neural network (ANN). Ductility database were prepared, and three parameters, namely, alloy type, grain size, and heat treatment cycle were selected for modeling. Additionally, ductility data were generated from the literature for training and validation of models on the basis of linearity and considering the primary effect of these three parameters. Model was trained and tested for three different datasets drawn from the generated data. Possibility of improving ductility by more than 5% is observed for multicomponent alloy with grain size of 10–50 μm following a multistep heat treatment cycle.http://dx.doi.org/10.1155/2011/874375 |
| spellingShingle | R. K. Gupta Rama Mehta Vijaya Agarwala Bhanu Pant P. P. Sinha Study on Ductility of Ti Aluminide Using Artificial Neural Network International Journal of Aerospace Engineering |
| title | Study on Ductility of Ti Aluminide Using Artificial Neural Network |
| title_full | Study on Ductility of Ti Aluminide Using Artificial Neural Network |
| title_fullStr | Study on Ductility of Ti Aluminide Using Artificial Neural Network |
| title_full_unstemmed | Study on Ductility of Ti Aluminide Using Artificial Neural Network |
| title_short | Study on Ductility of Ti Aluminide Using Artificial Neural Network |
| title_sort | study on ductility of ti aluminide using artificial neural network |
| url | http://dx.doi.org/10.1155/2011/874375 |
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