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 \( \left( {V_{c} } \right) \), feed rate (f), depth of cut (ap) and nose radius \( \left( {N_{r} } \right) \) 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 \( \left( {R_{a} } \right) \) 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 cm³/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.

为了解决与加工聚四氟乙烯有关的多目标问题的最优解，提出了一种基于非支配排序的改进的基于教学的学习优化（NSMTLBO）。首先，进行了实验设计，并以三级切削速度\（\ left（{V_ {c}} \ right）\），进给速度（f），切削深度（ap）和刀鼻进行加工的L27正交阵列半径\（\ left（{N_ {r}} \ right）\）制定。使用数控车床以硬质合金刀具以80°的插入角进行实验，并测量响应变量，即表面光洁度和材料去除率。从实验结果中得出响应表面模型，以得出表面粗糙度的最小化函数\（\ left（{R_ {a}} \ right）\）和材料去除率（MRR）的最大化函数。使用NSMTLBO同时解决这两个优化功能。模糊决策者还与NSMTLBO集成在一起，根据每个目标函数的相对重要性级别从Pareto前端确定首选的最佳加工参数。最佳响应R _a  = 2.2347 µm和MRR 在V _c  = 160 mm / min，f  = 0.5 mm / rev，ap  = 0.98 mm和N _r  = 0.8 mm的最佳加工参数下，可预测出= 96.835 cm ³ / min 。据报道，拟议的NSMTLBO优于其他六种对等算法，这是因为它在生成Pareto前沿方面表现出了卓越的能力，该Pareto前沿分布更均匀，并且导致非控制解的百分比更高。此外，NSTTLBO的预测结果已通过实验验证，据报道，预测结果与实际结果之间的性能偏差低于3.7％，这意味着所提出的工作可在实际的机加工应用中使用。