Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel
In this work, an Artificial Neural Network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and chip serration frequency during high speed end milling of medium carbon steel (S45C). The input parameters of the ANN model are the cutting par...
| 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/17330/ http://irep.iium.edu.my/17330/1/CUTSE_Sarawak.pdf |
| _version_ | 1848778026305716224 |
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| author | Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Ginta, Turnad Lenggo Alam, S. Lajis, M. A. |
| author_facet | Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Ginta, Turnad Lenggo Alam, S. Lajis, M. A. |
| author_sort | Patwari, Muhammed Anayet Ullah |
| building | IIUM Repository |
| collection | Online Access |
| description | In this work, an Artificial Neural Network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and chip serration frequency during high speed end milling of medium carbon steel (S45C). The input parameters of the ANN model are the cutting parameters: cutting speed, feed, and axial depth of cut. The output parameter of the model was chip serration frequency. 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 central composite design experimental design technique. A predictive model for chip serration frequency was created using a feed-forward backpropagation neural network exploiting experimental data. The network was trained with pairs of inputs/outputs datasets generated when end milling steel with TiN 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 chip serration frequency in metal-cutting operations. |
| first_indexed | 2025-11-14T14:55:20Z |
| format | Proceeding Paper |
| id | iium-17330 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T14:55:20Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-173302012-04-10T06:33:07Z http://irep.iium.edu.my/17330/ Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Ginta, Turnad Lenggo Alam, S. Lajis, M. A. TS Manufactures In this work, an Artificial Neural Network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and chip serration frequency during high speed end milling of medium carbon steel (S45C). The input parameters of the ANN model are the cutting parameters: cutting speed, feed, and axial depth of cut. The output parameter of the model was chip serration frequency. 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 central composite design experimental design technique. A predictive model for chip serration frequency was created using a feed-forward backpropagation neural network exploiting experimental data. The network was trained with pairs of inputs/outputs datasets generated when end milling steel with TiN 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 chip serration frequency in metal-cutting operations. 2008-11 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/17330/1/CUTSE_Sarawak.pdf Patwari, Muhammed Anayet Ullah and Amin, A. K. M. Nurul and Faris, Waleed Fekry and Ginta, Turnad Lenggo and Alam, S. and Lajis, M. A. (2008) Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel. In: International Conference of Curtin University of Science and Technology Engineering (CUTSE 2008), 24 - 27 November 2008, Sarawak, Malaysia. http://www.curtin.edu.my/index.htm |
| spellingShingle | TS Manufactures Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Ginta, Turnad Lenggo Alam, S. Lajis, M. A. Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel |
| title | Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel |
| title_full | Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel |
| title_fullStr | Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel |
| title_full_unstemmed | Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel |
| title_short | Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel |
| title_sort | development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel |
| topic | TS Manufactures |
| url | http://irep.iium.edu.my/17330/ http://irep.iium.edu.my/17330/ http://irep.iium.edu.my/17330/1/CUTSE_Sarawak.pdf |