Computational analysis for the prediction chip serration frequency in end milling of steel AISI1020

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...

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Main Authors: Patwari, Muhammed Anayet Ullah, Amin, A. K. M. Nurul, Faris, Waleed Fekry, Alam, S.
Format: Conference or Workshop Item
Language:English
Published: 2008
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://irep.iium.edu.my/16526/
http://irep.iium.edu.my/16526/1/OS04MMC.pdf
id iium-16526
recordtype eprints
spelling iium-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 Conference or Workshop Item 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.
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic TJ Mechanical engineering and machinery
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
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.
format Conference or Workshop Item
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
title 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_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_sort computational analysis for the prediction chip serration frequency in end milling of steel aisi1020
publishDate 2008
url http://irep.iium.edu.my/16526/
http://irep.iium.edu.my/16526/1/OS04MMC.pdf
first_indexed 2023-09-18T20:25:18Z
last_indexed 2023-09-18T20:25:18Z
_version_ 1777408376974606336

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