Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : response surface methodology (RSM)

Optimization of Plasma Cutting Parameters on Dimensional Accuracy and Machining Time for Low Carbon Steel

Sami A. Hamood; Vian N. Najm

Engineering and Technology Journal, 2020, Volume 38, Issue 8, Pages 1160-1168
DOI: 10.30684/etj.v38i8A.1151

This work aims to study the influence of plasma arc cutting parameters on dimensional accuracy and machining time for mild steel (1010) material with the thickness (4 mm). Selected three cutting parameters (arc current, cutting speed, and arc distance) or the experimental work. 12 tests have been performed at each test one parameter has been changed with four various levels and other parameters are constant. The influence of the cutting parameters on response results (dimensional accuracy and machining time) have been studied and analyzed by using response surface methodology (RSM) using the second-order model and main effect plot have been generated of each parameter on response results by using ANOVA depended on results of response surface analysis. The results of the response surface analysis showed that the important influencing parameters on dimensional accuracy were cutting speed and arc current as well as on deviation of dimensional accuracy, and the machining time is further affected with the current more than the cutting speed and the standoff distance. The outcomes of response surface analysis showed that the optimal setting of the cutting parameters to obtain at high dimensional accuracy were (arc current= 110 A, cutting speed = 4000 m/min, arc distance = 2mm) and to obtain on less machining time was (arc current= 110 A, cutting speed = 4000 m/min, arc distance = 5mm)

Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN

Hind H. Abdulridha; Aseel J. Helael; Ahmed A. Al-duroobi

Engineering and Technology Journal, 2020, Volume 38, Issue 6, Pages 887-895
DOI: 10.30684/etj.v38i6A.705

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.