Multi-Objective Optimization of Cutting Parameters in Hard Turning Using the NSGA-II Algorithm

Cutting tool machining occupies an important place among various material transformation techniques. It facilitates producing mechanical parts of complex shapes with very tight tolerances. However, the machining of hard materials suffers from the lack of knowledge on the physical phenomena involved during cutting. This paper describes a code based on the NSGA-II (non-dominated sorting genetic algorithm II) in order to choose optimal cutting parameter values. The objective functions expressing the three components of the cutting force are obtained by modeling experimental cutting data by multiple linear regression. The case study made it possible to prove the effectiveness of the described model. The result obtained is a set of optimal solutions (Pareto front) which offers the user many degrees of freedom and readability for the choice of a solution even more personalized to the user needs. The quality of the solutions obtained showcases possibilities of industrial applications.