Prediction the influence of machining parameters for CNC turning of aluminumalloy using RSM and ANN

Abstract EN

The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod.

The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology.

The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) wereanalyzed through analysis of variance (ANOVA).

The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut.

Surface response methodology developed between the machining parameters and response and confirmation experiments revealsthat the good agreement with the regression models.

The coefficient of determination value for RSM model is found to be high (R2 = 0.961).

It indicates the goodness of fit for the model and high significance of the model.

From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly.

However, if the test patterns number will be increased then this error can be further minimized.

The proposed RSM and ANN prediction model sufficiently predict Ra accurately.

However, ANN prediction model is found to be better compared to RSM model.

The artificial neutral network is applied to experimental results to find prediction results fortwo response parameters.

The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which producesflexibility to the manufacturing industries to select the best setting based on The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod.

The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology.

The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) wereanalyzed through analysis of variance (ANOVA).

The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut.

Surface response methodology developed between the machining parameters and response and confirmation experiments revealsthat the good agreement with the regression models.

The coefficient of determination value for RSM model is found to be high (R2 = 0.961).

It indicates the goodness of fit for the model and high significance of the model.

From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly.

However, if the test patterns number will be increased then this error can be further minimized.

The proposed RSM and ANN prediction model sufficiently predict Ra accurately.

However, ANN prediction model is found to be better compared to RSM model.

The artificial neutral network is applied to experimental results to find prediction results fortwo response parameters.

The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which producesflexibility to the manufacturing industries to select the best setting based on applications