Modelling and optimization of machining parameters during hardened steel AISID3 turning using RSM, ANN and DFA techniques: Comparative study
Keywords:Modelling, Optimization, RSM, ANN, DFA, response surface methodology, artificial neural network, desirability function analysis
Nowadays, the relationships linking the cutting conditions to the different technological parameters are becoming a major industrial objective. The present work deals with some machinability investigations involving surface roughness and cutting force in finish turning of AISI D3-hardened steel using carbide, ceramic and coated ceramic inserts. The combined effects of the cutting parameters represented by the cutting depth (ap), the cutting tool (Tool), the cutting speed (Vc), and the feed rate (f) on the output parameters illustrated by both the surface roughness (Ra) and the cutting force (Fy) are investigated using the analysis of variance (ANOVA). The modelling of surface roughness and cutting force is carried out using both Response Surface Methodology (RSM) and Artificial Neural Network (ANN). In order to determine the most efficient technique, the models developed are compared in terms of the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Model Predictive Error (MPE). Furthermore, a multi-objective optimization using the Desirability Function Analysis (DFA) has been performed. The obtained results show that the ANN models (For Ra: R²=93%, RMSE=0.02%, MPE=4.99% and for Fy: R²=94%, RMSE=2.52%, MPE=3.11%) performed better than those developed using the RSM approach (For Ra: R²=90.4%, RMSE=0.051%, MPE=18.21% and for Fy: R²=79%, RMSE=31.17%, MPE=55.92%). As a consequence, the achieved ANN models for (Ra) and (Fy) are exploited as objective function for the response optimization applying the DFA technique. The optimum level of the input parameters for the combined desirability is finally identified as ap1–Tool3–Vc3–f1 for both (Ra) and (Fy) with a maximum error of 2.94%.