When cubic boron nitride (CBN) tools are used for hard cutting hardened steel, a large cutting force is generated. This is accompanied by a large amount of cutting heat, resulting in serious tool wear. In this study, we combined surface weaving technology with hard cutting. Design simulations and experiments were performed on three-factor untextured orthogonal cutting by considering parameters such as depth of cut, cutting speed, and feed. Subsequently, the simulation and experimental data were analyzed by using the polar difference, variance, and signal-to-noise ratio methods to determine the best combination of cutting parameters and the degree of influence of each parameter on the cutting force generated during hard cutting. Hardened GCr15 steel with preset surface texture was hard cut using the optimal combination of cutting parameters. Consequently, tool wear was observed, and the cutting forces were measured. The results were compared with those obtained from the equivalent cutting conditions without texture. The results revealed that a minimum cutting force of 71.48 N was obtained in the untextured cutting experiment. Additionally, a minimum cutting force of 44.64 N was measured in the preset surface texture cutting experiment under the same conditions, which was approximately 37.55% lower than the result obtained in the untextured cutting experiment. The tool wear was much higher for untextured cutting than for preset surface texture cutting. The comparative experiments performed, with and without texture, indicated that a combination of surface texture technology and hard cutting can effectively reduce the cutting forces and tool wear and improve tool life.

In order to improve the efficiency of high-speed machining center and shorten its warm-up time, it is realistic and feasible to measure the thermal deformation of the machine tool system and then improve the machining accuracy of the machine by means of compensation. In this paper, a model XKA714B/A CNC milling machine and a 10mm diameter ball-head milling tool are selected. A high-speed camera is used to capture the gray level images of the tool when the machining center spindle speed is working at 1000 r/min. Using MATLAB software, the image edge extraction is coarsely localized by Canny algorithm, and sub-pixel fitting edge detection method is used to precisely locate the tool edge profile. The least-squares method is applied to fit the tool tip circular curve so as to calculate the thermal deformation during the tool preheating process. The results showed that there is a certain connection between the thermal deformation of the tool and the machine running time during the preheating process of the machine tool. That is, in the initial stage of machine operation, the tool axial thermal deformation is larger. In the 6th to 26th min, the tool thermal deformation gradually becomes smaller. At the 26th minute of preheating, the tool deformation reached more than 96% of the total deformation and the deformation rate leveled off. The axial deformation of the tool was measured to be 130.2 um at this time. Inputting the measurement results into the machining center tool holder control system as the compensation value will shorten the machine warm-up and thermal balance time so as to ensure its machining accuracy, which is of practical significance to improve machining efficiency and reduce cost in the actual production process.