Multiobjective optimization of machining condition in finish hard turning 9XC steel with CBN cutting tool using genetic algorithms

This paper presents a research on multiobjective optimization of machining parameters in finished hard turning 9XC alloy steel with CBN cutting tool using genetic algorithms. Two mutually conflicting objectives are optimized concurrently: surface roughness, which denotes the surficial quality, and machined surface area, which represents the tool life. Models of surface roughness and machined area were achieved from regression analysis of experimental data. The multiobjective optimization using genetic algorithms obtained optimal Pareto set. Also the compromise solution that is as close as possible to the utopia point is determined. Analyzing the outcomes shows that in the domain investigation, cutting speed has significant effects on optimal situation of machining solutions. Optimal values of cutting speed concentrated around 100m/min. The changing deep of cut and feed rate within probe area has negligible effects on optimal situation of solutions.