Yazar "Ragab, Adham E." seçeneğine göre listele

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    Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network
    (Hindawi Ltd, 2017) Alharthi, Nabeel H.; Bingol, Sedat; Abbas, Adel T.; Ragab, Adham E.; El-Danaf, Ehab A.; Alharbi, Hamad F.
    In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values. Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.
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    Öğe
    Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network
    (Hindawi Ltd, 2018) Alharthi, Nabeel H.; Bingol, Sedat; Abbas, Adel T.; Ragab, Adham E.; Aly, Mohamed F.; Alharbi, Hamad F.
    All manufacturing engineers are faced with a lot of difficulties and high expenses associated with grinding processes of AZ61. For that reason, manufacturing engineers waste a lot of time and effort trying to reach the required surface roughness values according to the design drawing during the turning process. In this paper, an artificial neural network (ANN) modeling is used to estimate and optimize the surface roughness (R-a) value in cutting conditions of AZ61 magnesium alloy. A number of ANN models were developed and evaluated to obtain the most successful one. In addition to ANN models, traditional regression analysis was also used to build a mathematical model representing the equation required to obtain the surface roughness. Predictions from the model were examined against experimental data and then compared to the ANN model predictions using different performance criteria such as the mean absolute error, mean square error, and coeffcient of determination.