| Autor: |
Gupta, N., Taneja, V., Rajora, V. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 10/11/2022, Vol. 2653 Issue 1, p1-6, 6p |
| Abstrakt: |
Wire EDM is most commonly used for extruding plastics molds and press dies. The inherent advantages are very high surface finish coupled with corner machining and small radius making especially in complex shapes. It's been widely used in defence, aerospace, automotive sector etc. The optimization of input process parameters in WEDM is necessary to arrive at optimal output characteristics of the process. Artificial Neural Network is one of upcoming techniques to optimize input process parameters in manufacturing operations. In this analytical study, (MRR), Material removal rate is predicted with Artificial Neural Network and is compared with it's experimental derived values. A feed forward neural network with back propagation algorithm is trained to find better MRR. MATLAB program is used develop neural network model and trained model is simulated. The trial experiments were analyzed from an experimental study to get the experimental data to train the neural network. There was average cumulative error of 0.158 % between the experimental and the predicted values. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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