Non-dominated sorting modified teaching-learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)

A non-dominated sorting modified teaching-learning-based optimization (NSMTLBO) is proposed to obtain the optimum solution for a multi-objective problem related to machining Polytetrafluoroethylene. Firstly, an experimental design is done and the L27 orthogonal array with three-level of cutting speed mml:mfenced close=) open=(Vc$$ \left( {V_{c} } \right) $$\end{document}, feed rate (f), depth of cut (ap) and nose radius mml:mfenced close=) open=(Nr$$ \left( {N_{r} } \right) $$\end{document} is formulated. A CNC turning machine is used to perform experiments with cemented carbide tool at an insert angle of 80 degrees and the response variables known as surface finish and material removal rate are measured. A response surface model is rendered from the experimental results to derive the minimization function of surface roughness mml:mfenced close=) open=(Ra$$ \left( {R_{a} } \right) $$\end{document} and maximization function of material removal rate (MRR). Both optimization functions are solved simultaneously using NSMTLBO. A fuzzy decision maker is also integrated with NSMTLBO to determine the preferred optimum machining parameters from Pareto-front based on the relative importance level of each objective function. The best responses R-a = 2.2347 mu m and MRR = 96.835 cm(3)/min are predicted at the optimum machining parameters of V-c = 160 mm/min, f = 0.5 mm/rev, ap = 0.98 mm and N-r = 0.8 mm. The proposed NSMTLBO is reported to outperform other six peer algorithms due to its excellent capability in generating the Pareto-fronts which are more uniformly distributed and resulted higher percentage of non-dominated solutions. Furthermore, the prediction results of NSMTLBO are validated experimentally and it is reported that the performance deviations between the predicted and actual results are lower than 3.7%, implying the applicability of proposed work in real-world machining applications.