Modeling of Surface Roughness and Tool Wear in Turning using ANN and ANOVAJournal: International Journal of Science and Research (IJSR) (Vol.10, No. 5)
Publication Date: 2021-05-05
Authors : Shanavas K P;
Page : 775-782
Keywords : Neural network; Turning; Surface roughness;
In turning operation surface roughness and tool wear are important parameters. In manufacturing industry always trying to minimize the surface roughness and tool wear parameters. But it is not easy to determine which input parameters like cutting speed, feed, depth of cut, have minimum effect on the above parameters. Therefore it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness and tool wear. Back Propagation Neural Network (BPNN) structure is developed to predict surface roughness and tool wear. For modelling, generally ANN architectures, learning/ training algorithms and nos. of hidden neurons, transfer function are varied to achieve minimum error, but the variation is made in random manner. So here Taguchi method (ANOVA) has been implemented to achieve the optimal of above. The results obtained, conclude that ANN is reliable and accurate for solving the cutting parameter.
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Last modified: 2021-06-26 18:57:34