Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN | ||
| Engineering and Technology Journal | ||
| Article 10, Volume 38, 6A, June 2020, Pages 887-895 PDF (1.01 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.30684/etj.v38i6A.705 | ||
| Authors | ||
| Hind H. Abdulridha1; Aseel J. Helael* 1; Ahmed A. Al-duroobi2 | ||
| 1Production Engineering and Metallurgy Department, University of Technology-Iraq. | ||
| 2Prosthetics and Orthotics Engineering Department, College of Engineering, Al-Nahrain University, | ||
| Abstract | ||
| 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) were analyzed 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 reveals that 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 for two 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 produces flexibility to the manufacturing industries to select the best setting based on applications. | ||
| Keywords | ||
| response surface methodology (RSM); Surface roughness (Ra); Artificial Neural Network (ANN) | ||
| References | ||
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