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

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.

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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.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 2872/16526
Uncontrolled Keywords: Chip serration frequency, end milling, response surface methodology
Subjects: T Technology > TJ Mechanical engineering and machinery
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Manufacturing and Materials Engineering
Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User: Dr. A.K.M. Nurul Amin
Date Deposited: 10 Apr 2012 08:06
Last Modified: 10 Apr 2012 08:06
URI: http://irep.iium.edu.my/id/eprint/16526

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