Influence of tool characteristics on white layer produced by cutting hardened steel and prediction of white layer thickness

In the dry and hard cutting process of hardened steel, the white layer of the machined surface has a great influence on the service performance and life of the part. The cutting force and cutting heat produced in the process are among the important factors affecting the white layer characteristics. In the process of machining, in addition to the cutting parameters, the characteristics of cutting tools can also lead to significant changes in both service performance and part lifetime. Therefore, it is necessary to further study the changes in the cutting force, cutting heat, and white layer characteristics under the influence of the tool characteristics. In this paper, hard cutting tests of hardened steel were carried out by cutting tools (including PCBN tools and ceramic tools) with different thermal conductivities and flank wear under different cutting speeds. The influence of the cutting speed and tool characteristics on the cutting force, flank temperature and white layer characteristics was analyzed, and a prediction model for the white layer thickness was established. It was found that the cutting speed, tool wear degree, and tool thermal conductivity all have a significant influence on the cutting force, cutting temperature, and white layer thickness. Among the results, the thickness of the white layer first increases and then decreases with increasing flank temperature, and the critical temperature at the maximum thickness of the white layer is the actual final temperature of austenite transformation. Changes in the cutting force indirectly affect the temperatures of the austenite and martensite phase transitions, thus affecting the thickness of the white layer. The prediction results show that a fuzzy neural network based on particle swarm optimization can effectively predict the thickness of the white layer.

Automated summary

This study investigates the influence of tool characteristics on cutting force, cutting temperature, and white layer characteristics in the hard cutting of hardened steel, and establishes a prediction model for white layer thickness. The research shows that cutting speed, tool wear degree, and thermal conductivity significantly affect the machining outcomes.