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Ti-6Al-4V alloy has low thermal conductivity, poor machinability, and active chemical activity. However, it is easily bonded to tool surface in the cutting process, resulting in drastic tool wear. The friction contact state and friction behavior between the tool and machined surface can be significantly improved when adopting tool with micro texture on the surface. The tools with line texture, regular hexagonal texture, and without texture on the rake face were adopted in this study. The cutting force, tool wear morphology, and friction coefficient of different type of tools are analyzed based on dry cutting test, friction, and wear test of cemented carbide and titanium alloy. The result shows that the cutting force caused by the line-textured tool is the smallest, following by hexagonal-textured tool and no-textured tool. However, large damage would happen at the cutting edge of line-textured tool as the cutting continues, while the damage of hexagonal-textured tool is relatively light. The main cutting force and feed force of the three kinds of tools increase as cutting depth increased, while decrease with the increase of cutting speed. The wear morphology of textured tool is smoother than that of no-textured tool, due to the micro texture can contain debris and reduce friction coefficient. A cutting force equation is established by ridge regression method based on actual cutting data, which provides a new idea for monitoring the tool during cutting process.

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.