Modeling the effect of CNT concentration in dielectric fluid on EDM performance using neural network
Electrical discharge machining (EDM) is one of the most reliable and precise manufacturing processes that applicable for creating complex geometries. However, the high heat produce on the electrically discharged material during the EDM process will minimize the surface quality of final product. Carb...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
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The Institute of Electrical and Electronics Engineers, Inc.
2016
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| Online Access: | http://irep.iium.edu.my/47432/ http://irep.iium.edu.my/47432/1/47432_modeling_the_effect_of_CNT.pdf http://irep.iium.edu.my/47432/4/47432_Modeling%20the%20Effect_Scopus.pdf |
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| author | Khan, Ahsan Ali Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas Mohd. Fauzey, Nur Fadhilah |
| author_facet | Khan, Ahsan Ali Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas Mohd. Fauzey, Nur Fadhilah |
| author_sort | Khan, Ahsan Ali |
| building | IIUM Repository |
| collection | Online Access |
| description | Electrical discharge machining (EDM) is one of the most reliable and precise manufacturing processes that applicable for creating complex geometries. However, the high heat produce on the electrically discharged material during the EDM process will minimize the surface quality of final product. Carbon nanotubes possess unexpected strength and unique electrical and thermal properties. On the account of this, multi-wall carbon nanotubes (MWCNTs) are added to the dielectric used in the EDM process to improve its performance when machining the stainless steel using copper electrodes. In this research the effect of the concentration of MWCNTs and the peak current on the surface quality of stainless steel was investigated. The experimental result gained proved that addition of MWCNTs reduce the surface roughness and increase the material removal rate of work material. Due the number of experiment, the experiments have been simulated using JMP simulator with 500 run using the real results. The simulation results have been used in next step to develop the neural network model. The validation between the predicted and the simulation reseals show a high accuracy. |
| first_indexed | 2025-11-14T16:16:41Z |
| format | Proceeding Paper |
| id | iium-47432 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T16:16:41Z |
| publishDate | 2016 |
| publisher | The Institute of Electrical and Electronics Engineers, Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-474322016-12-20T04:02:47Z http://irep.iium.edu.my/47432/ Modeling the effect of CNT concentration in dielectric fluid on EDM performance using neural network Khan, Ahsan Ali Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas Mohd. Fauzey, Nur Fadhilah TS200 Metal manufactures. Metalworking Electrical discharge machining (EDM) is one of the most reliable and precise manufacturing processes that applicable for creating complex geometries. However, the high heat produce on the electrically discharged material during the EDM process will minimize the surface quality of final product. Carbon nanotubes possess unexpected strength and unique electrical and thermal properties. On the account of this, multi-wall carbon nanotubes (MWCNTs) are added to the dielectric used in the EDM process to improve its performance when machining the stainless steel using copper electrodes. In this research the effect of the concentration of MWCNTs and the peak current on the surface quality of stainless steel was investigated. The experimental result gained proved that addition of MWCNTs reduce the surface roughness and increase the material removal rate of work material. Due the number of experiment, the experiments have been simulated using JMP simulator with 500 run using the real results. The simulation results have been used in next step to develop the neural network model. The validation between the predicted and the simulation reseals show a high accuracy. The Institute of Electrical and Electronics Engineers, Inc. 2016 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/47432/1/47432_modeling_the_effect_of_CNT.pdf application/pdf en http://irep.iium.edu.my/47432/4/47432_Modeling%20the%20Effect_Scopus.pdf Khan, Ahsan Ali and Al Hazza, Muataz Hazza Faizi and Adesta, Erry Yulian Triblas and Mohd. Fauzey, Nur Fadhilah (2016) Modeling the effect of CNT concentration in dielectric fluid on EDM performance using neural network. In: 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2015), 8th-10th Dec. 2015, Kuala Lumpur. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7478756 10.1109/ACSAT.2015.24 |
| spellingShingle | TS200 Metal manufactures. Metalworking Khan, Ahsan Ali Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas Mohd. Fauzey, Nur Fadhilah Modeling the effect of CNT concentration in dielectric fluid on EDM performance using neural network |
| title | Modeling the effect of CNT concentration in dielectric fluid on EDM performance using neural network |
| title_full | Modeling the effect of CNT concentration in dielectric fluid on EDM performance using neural network |
| title_fullStr | Modeling the effect of CNT concentration in dielectric fluid on EDM performance using neural network |
| title_full_unstemmed | Modeling the effect of CNT concentration in dielectric fluid on EDM performance using neural network |
| title_short | Modeling the effect of CNT concentration in dielectric fluid on EDM performance using neural network |
| title_sort | modeling the effect of cnt concentration in dielectric fluid on edm performance using neural network |
| topic | TS200 Metal manufactures. Metalworking |
| url | http://irep.iium.edu.my/47432/ http://irep.iium.edu.my/47432/ http://irep.iium.edu.my/47432/ http://irep.iium.edu.my/47432/1/47432_modeling_the_effect_of_CNT.pdf http://irep.iium.edu.my/47432/4/47432_Modeling%20the%20Effect_Scopus.pdf |