Enhanced Multi-Objective Teaching-Learning-Based Optimization for Machining of Delrin

This paper deals with the optimization of machining parameters of speed, feed rate, and depth of cut that aim to simultaneously achieve the low surface roughness (SR) and high material removal rate (MRR) of a version of ACETAL homopolymer material known as Delrin. First, an L27 orthogonal array with three-level of cutting speed (<inline-formula> <tex-math notation="LaTeX">$V_{c}$ </tex-math></inline-formula>), feed rate (<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>), and depth of cut (ap) is formulated, and the experiments are conducted accordingly in a CNC turning machine using cemented carbide tool with insert angle of 80°. A response surface model is rendered from these experimental results, and two objective functions representing the SR and MRR of Delrin are derived. An enhanced multi-objective teaching–learning-based optimization (EMOTLBO) is then proposed to solve the multi-objective machining problem, aiming to minimize the SR and maximize the MRR of Delrin simultaneously. A fuzzy decision maker is also integrated to softly select the preferred solution from Pareto-front based on the importance level of both objective functions. Extensive simulation studies prove that EMOTLBO is more competitive than other existing algorithms for being able to produce a more uniformly distributed Pareto-front. Simulation results are further validated Experimentally, and the difference of lower than 5% is observed that imply to good agreement between the simulation and experimental results.