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Artificial Neural Network for Predicting Machining Performance of Uncoated Carbide (WC-Co) in Milling Machining Operation

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3 Author(s)
Zain, A.M. ; Fac. of Comput. Sci. & Inf. Syst., Univ. Teknol. Malaysia, Skudai, Malaysia ; Haron, H. ; Sharif, S.

Surface roughness (Ra) is one of the most common responses in machining and an effective parameter to represent the quality of a machined surface. This paper presents the capability of an Artificial Neural Network (ANN) technique to develop a model to predict the Ra value of milling process. The model, presented as a network structure, is developed using the MATLAB ANN toolbox. Four different network structures were developed and assessed. The result of the modeling shows that a 3-7-1 network structure is the best model for end milling a titanium alloy using an uncoated carbide (WC-Co) cutting tool. The result of the ANN model has been compared to the experimental result, and ANN gave a good agreement between predicted and experimentally measured process parameters. The ANN technique has decreased the minimum surface roughness value of the experimental sample data by about 0.0126 ¿m, or 5.33%.

Published in:

Computer Technology and Development, 2009. ICCTD '09. International Conference on  (Volume:1 )

Date of Conference:

13-15 Nov. 2009