Mohd. Zain, Azlan and Haron, Habibollah and Sharif, Safian (2009) Artificial neural network for predicting machining performance of uncoated carbide (WC-CO) in miling machining operation. In: International Conference on Computer Technology and Development (ICCTD 2009), 2009, Kota Kinabalu, Malaysia.
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Official URL: http://dx.doi.org/10.1109/ICCTD.2009.98
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 Ã?Â¿m, or 5.33%.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||ANN, machining, modeling, surface roughness|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System|
|Deposited By:||Liza Porijo|
|Deposited On:||14 Sep 2011 04:40|
|Last Modified:||14 Sep 2011 04:40|
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