Bi-performance optimisation of machining parameters of reinforced PEEK composites using NSGA-II

Because of their effectiveness and robustness in searching for global solutions, optimisations using evolutionary algorithm (EA) techniques are presently gaining significant attention in various fields. They are reported to be suitable for achieving multiple objective optimisations. This work is dedicated to machining process of PEEK CF30 using TiN coated tools under dry conditions. The influence on machining force and specific cutting pressure is analysed as function of the main operating parameters which include cutting speed, depth of cut and feed rate. The obtained experimental results were used at first to develop statistical models that are based on second order polynomial regression for the considered process characteristics. Then, the non-dominated sorting genetic algorithm (NSGA-II) was used to optimise the processing conditions. A non-dominated solution set that minimise both cutting force and specific pressure was determined.