Comparative Analysis and Optimization of FEM and RSM Based Regression Model with Experimental Results for the Dry Turning of SiCp- Al7075 Composite

Abstract

The paper presents a finite element based methodology by converting full scale turning operation to a two-dimensional plain strain machining model. The method is used for predicting the output cutting parameters by using the Johnson cook elastic and fracture model to improve the prediction capabilities for industrial applications. For material modeling purposes, ABAQUS Explicit module is used by taking coupled temperature displacement elements. Different input cutting regimes, i.e., feed rate, depth of cut, and cutting speed, have been varied to evaluate the values of cutting force, cutting temperature and also the effect of tool particle interaction for the turning of SiC (100 μm, 10% wt.) reinforced Al7075 composite. Moreover, to view the effect of variation of material and dimensional property fracture energy has been varied. Further, the response surface model is developed by using full factorial design to generate the regression equations for the estimation of output variables within the selected input variables range. A number of 256 different simulations were executed, out of which 230 were used for the training purpose, and 26 for validation purposes. Results depict the prediction error of the regression model for validatory runs, which varies from 0.21 to 17.13% for Fc, Ft, and Tt, respectively. The finite element model is validated with experimental analysis by examining a set of 8 random runs. Prediction accuracy based on the above comparison varies from 83.33 to 93.68% for cutting force components and cutting temperature. It is also observed that the finite element model and the response surface model are having a good agreement with experimental results.