Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process
Artificial neural networks (ANN) are used in distinct researching fields and professions, and are prepared by cooperation of scientists in different fields such as computer engineering, electronic, structure, biology and so many different branches of science. Many models are built correlating the...
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World Academy of Science, Engineering and Technology
2013
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| Online Access: | http://umpir.ump.edu.my/id/eprint/6458/ http://umpir.ump.edu.my/id/eprint/6458/ http://umpir.ump.edu.my/id/eprint/6458/1/Artificial_Intelligent_Approach_for_Machining_Titanium_Alloy_in_a_Nonconventional_Process.pdf |
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eprints |
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ump-64582018-01-25T03:49:00Z http://umpir.ump.edu.my/id/eprint/6458/ Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process Khan, Md. Ashikur Rahman M. M., Rahman K., Kadirgama TJ Mechanical engineering and machinery Artificial neural networks (ANN) are used in distinct researching fields and professions, and are prepared by cooperation of scientists in different fields such as computer engineering, electronic, structure, biology and so many different branches of science. Many models are built correlating the parameters and the outputs in electrical discharge machining (EDM) concern for different types of materials. Up till now model for Ti-5Al-2.5Sn alloy in the case of electrical discharge machining performance characteristics has not been developed. Therefore, in the present work, it is attempted to generate a model of material removal rate (MRR) for Ti-5Al-2.5Sn material by means of Artificial Neural Network. The experimentation is performed according to the design of experiment (DOE) of response surface methodology (RSM). To generate the DOE four parameters such as peak current, pulse on time, pulse off time and servo voltage and one output as MRR are considered. Ti-5Al-2.5Sn alloy is machined with positive polarity of copper electrode. Finally the developed model is tested with confirmation test. The confirmation test yields an error as within the agreeable limit. To investigate the effect of the parameters on performance sensitivity analysis is also carried out which reveals that the peak current having more effect on EDM performance. World Academy of Science, Engineering and Technology 2013 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6458/1/Artificial_Intelligent_Approach_for_Machining_Titanium_Alloy_in_a_Nonconventional_Process.pdf Khan, Md. Ashikur Rahman and M. M., Rahman and K., Kadirgama (2013) Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process. International Journal of Mathematical, Computational, Physical and Quantum Engineering, 7 (11). pp. 1122-1127. http://waset.org/publications/9997233/artificial-intelligent-approach-for-machining-titanium-alloy-in-a-nonconventional-process- |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
TJ Mechanical engineering and machinery |
| spellingShingle |
TJ Mechanical engineering and machinery Khan, Md. Ashikur Rahman M. M., Rahman K., Kadirgama Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process |
| description |
Artificial neural networks (ANN) are used in distinct
researching fields and professions, and are prepared by cooperation of scientists in different fields such as computer engineering, electronic, structure, biology and so many different branches of science. Many models are built correlating the parameters and the outputs in electrical discharge machining (EDM) concern for different types of materials. Up till now model for Ti-5Al-2.5Sn alloy in the case of electrical discharge machining performance characteristics has not been developed. Therefore, in the present work, it is attempted to generate a model of material removal rate (MRR) for Ti-5Al-2.5Sn material by means of Artificial Neural Network. The experimentation is performed according to the design of experiment (DOE) of response surface methodology (RSM). To generate the DOE four parameters such as peak current, pulse on time, pulse off time and servo voltage and one output as MRR are considered. Ti-5Al-2.5Sn alloy is machined with positive polarity of copper electrode. Finally the developed model is tested with confirmation test. The confirmation test yields an error as within the agreeable limit. To investigate the effect of the parameters on performance sensitivity analysis is also carried out which reveals that the peak current having more effect on EDM performance. |
| format |
Article |
| author |
Khan, Md. Ashikur Rahman M. M., Rahman K., Kadirgama |
| author_facet |
Khan, Md. Ashikur Rahman M. M., Rahman K., Kadirgama |
| author_sort |
Khan, Md. Ashikur Rahman |
| title |
Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process |
| title_short |
Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process |
| title_full |
Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process |
| title_fullStr |
Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process |
| title_full_unstemmed |
Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process |
| title_sort |
artificial intelligent approach for machining titanium alloy in a nonconventional process |
| publisher |
World Academy of Science, Engineering and Technology |
| publishDate |
2013 |
| url |
http://umpir.ump.edu.my/id/eprint/6458/ http://umpir.ump.edu.my/id/eprint/6458/ http://umpir.ump.edu.my/id/eprint/6458/1/Artificial_Intelligent_Approach_for_Machining_Titanium_Alloy_in_a_Nonconventional_Process.pdf |
| first_indexed |
2023-09-18T22:02:12Z |
| last_indexed |
2023-09-18T22:02:12Z |
| _version_ |
1777414473477259264 |