LAUSR

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 $$ \left( {V_{c} } \right) $$ V c , feed rate ( f ), depth of cut ( ap ) and nose radius $$ \left( {N_{r} } \right) $$ N r is formulated. A CNC turning machine is used to perform experiments with cemented carbide tool at an insert angle of 80° 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 $$ \left( {R_{a} } \right) $$ R a 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 µm 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.