Optimization of CNC Machining Parameters using ANOVA and Artificial Neural Network
Keywords:
Surface Roughness, Material removal rate, ANN (Artificial Neural Network), ANOVA Analysis of variance and Regression modelingAbstract
In the present study, the influence of machining parameters on surface roughness and material removal rate is examined by utilizing ANN& ANOVA techniques. Three important variables i.e. spindle velocity, depth of cut and feed rate which are influence on the surface roughness and material removal rate are examined and also analyzed. Artificial Neural Network and Analysis of variance techniques are effective tools for analyze and optimize the cutting parameters. Based on taguchi, design of experiments, L27 orthogonal array was selected for conducting turning experiments. 3 factors are considered at 3 levels for orthogonal array L27 design. The experimentation has been conducted on Aluminum alloy AL 6253 using CNC turner with carbide tip tool and experimental results are taken for preparing of the ANN model. The experimental results were analyzed by using ANOVA and the regression equation for predicting the surface roughness and MRR.
How to cite this article:
Kumar G. Optimization of CNC Machining Parameters using ANOVA and Artificial Neural Network. J Adv Res Prod Ind Engg 2020; 7(3&4): 6-12.
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