Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020
The output parameter of the model was surface roughness. For this interpretation, advantages of statistical experimental design technique, experimental measurements, artificial neural network were exploited in an integrated manner. Cutting experiments are designed based on statistical three-level fu...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2008
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/16526/1/OS04MMC.pdf |
| _version_ | 1825645638368362496 |
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| author | Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Alam, S. |
| author_facet | Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Alam, S. |
| author_sort | Patwari, Muhammed Anayet Ullah |
| collection | IIUM |
| description | The output parameter of the model was surface roughness. For this interpretation, advantages of statistical experimental design technique, experimental measurements, artificial neural network were exploited in an integrated manner. Cutting experiments are designed based on statistical three-level full factorial experimental design technique. A predictive model for surface roughness was created using a feed-forward back-propagation neural network exploiting experimental data. The network was trained with pairs of inputs/outputs datasets generated when end milling Inconel 718 alloy with single-layer PVD TiAlN coated carbide inserts. A very good predicting performance of the neural network, in terms of concurrence with experimental data was attained. The model can be used for the analysis and prediction for the complex relationship between cutting conditions and the surface roughness in metal-cutting operations and for the optimization of the surface roughness for efficient and economic production.
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| first_indexed | 2024-03-05T22:50:44Z |
| format | Proceeding Paper |
| id | oai:generic.eprints.org:16526 |
| institution | International Islamic University Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:50:44Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:165262012-04-10T00:06:57Z http://irep.iium.edu.my/16526/ Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020 Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Alam, S. TJ Mechanical engineering and machinery The output parameter of the model was surface roughness. For this interpretation, advantages of statistical experimental design technique, experimental measurements, artificial neural network were exploited in an integrated manner. Cutting experiments are designed based on statistical three-level full factorial experimental design technique. A predictive model for surface roughness was created using a feed-forward back-propagation neural network exploiting experimental data. The network was trained with pairs of inputs/outputs datasets generated when end milling Inconel 718 alloy with single-layer PVD TiAlN coated carbide inserts. A very good predicting performance of the neural network, in terms of concurrence with experimental data was attained. The model can be used for the analysis and prediction for the complex relationship between cutting conditions and the surface roughness in metal-cutting operations and for the optimization of the surface roughness for efficient and economic production. 2008-12-03 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/16526/1/OS04MMC.pdf Patwari, Muhammed Anayet Ullah and Amin, A. K. M. Nurul and Faris, Waleed Fekry and Alam, S. (2008) Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020. In: Malaysian Metallurgical Conference, MMC 2008, 3 - 4 December 2008, UKM, Bangi, Malaysia. |
| spellingShingle | TJ Mechanical engineering and machinery Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Alam, S. Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020 |
| title | Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020
|
| title_full | Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020
|
| title_fullStr | Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020
|
| title_full_unstemmed | Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020
|
| title_short | Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020
|
| title_sort | computational analysis for the prediction chip serration frequency in end milling of steel aisi1020 |
| topic | TJ Mechanical engineering and machinery |
| url | http://irep.iium.edu.my/16526/1/OS04MMC.pdf |
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