|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|138495||2018||8 صفحه PDF||سفارش دهید||3418 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Materials Today: Proceedings, Volume 5, Issue 1, Part 1, 2018, Pages 772-779
Electro-chemical machining (ECM) uses a set of intricate process to create a negative image of tool on workpiece by high rate anodic dissolution. A Full factorial (DOE) 24 is applied to determining the most important factors which influence MRR of AA6061 (T6). In the present work, experimental data collected are tested with analysis of variance (ANOVA) and Artificial Neural Network (ANN) model has been proposed for the prediction of response. For this purpose, the MATLAB has been used for training and testing of neural network model. The predicted results using ANN specify good agreement between the predicted values and experimental values. Multilayer perceptron model has been constructed with back-propagation algorithm using four process parameters viz. voltage, feed rate, electrolyte concentration and electrode (Cu, Brass) are considered in this study. Finally, ANN model has been found efficient to predict ECM process response for selected process conditions.