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Prediction of Electrochemical Machining Process Parameters using Artificial Neural Networks

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Author(s): Hoda Hosny Abuzied | Mohamed Ahmed Awad | Hesham Aly Senbel

Journal: International Journal on Computer Science and Engineering
ISSN 0975-3397

Volume: 4;
Issue: 1;
Start page: 125;
Date: 2012;
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Keywords: Electrochemical Machining | Artificial neural networks | Matlab neural networks toolbox.

ABSTRACT
Electrochemical machining (ECM) is a non-traditional machining process used mainly to cut hard or difficult to cut metals, where the application of a more traditional process is not convenient. It offers several special advantages including higher machining rate, better precision and control, and a wider range of materials that can be machined. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. So, artificial neural networks were introduced as an efficient approach to predict the values of resulting surface roughness and material removal rate. Many researchers usedartificial neural networks (ANN) in improvement of ECM process and also in other nontraditional machining processes as well be seen in later sections. The present study is, initiated to predict values of some of resulting process parameters such as metal removal rate(MRR), and surface roughness (Ra) using artificial neural networks based on variation of certain predominant parameters of an electrochemical broaching process such as applied voltage, feed rate and electrolyte flow rate. ANN was found to be an efficient approach as it reduced time & effort required to predict material removal rate & surface roughness if they were found experimentally using trial & error method. To validate the proposed approach the predicted values of surface roughness and material removal rate were compared with a previously obtained ones from the experimental work.
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