Analysis and optimization of hard turning operation using cubic boron nitride tool

The present work concerns an experimental study of hard turning of AISI 52100 bearing steel, with CBN tool. The combined effects of process parameters (cutting speed, feed rate, depth of cut and cutting time) on performance characteristics (tool wear, surface roughness, cutting forces and metal volume removed) are investigated using ANOVA analysis. The relationship between process parameters and performance characteristics through the response surface methodology (RSM) are modeled. Moreover, Grey-Taguchi method, composite desirability function and genetic algorithm are used as multi-objective optimization approaches to find the process parameters values that optimize simultaneously the performance characteristics. The results show that the cutting speed exhibits maximum influence on abrasive tool wear. The depth of cut affects strongly the cutting forces; however, it has a negligible influence on surface roughness. The cutting time has a considerable effect on all performance characteristics. Though the optimization approaches predicted near similar results, the GA technique seems to be the most advantageous approach. Finally, the proposed experimental and integrated approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes.