This paper presents a novel approach to measure the cutting temperature in process and control it to some extent by using an internally cooled smart cutting tool with a closed internal cooling circuitry. Numerical modeling based on the finite element analysis-computational fluid dynamics (CFD) method is carried out by using ansys and fluent, then the surface temperature distribution of the tool is fitted and the equivalent heat transfer coefficient of the tool surface contacting with cooling fluid is computed. Analytical thermal model of the tool is established based on the lumped parameter method. Theoretical analysis and numerical simulation results are in good agreement, which demonstrate that the innovative smart tooling design concept can effectively sense the cutting temperature at the cutting tool tip in process and also be used to reduce and control the critical cutting temperature in cutting zone for adaptive machining of difficult-to-machine materials, such as titanium and Inconel alloys. Experimental cutting trials are carried out to further examine and validate the method and concept of applying the smart cutting tool system.

The traditional flood cooling method applied in the internal turning process has disadvantages, such as having a low cooling efficiency and being environmentally unfriendly. In the present work, an internal spray cooling turning tool was designed, and the performance was numerically and experimentally accessed. The heat transfer simulation model of the internal spray cooling turning tool was established by ANSYS Fluent, and the tool cooling structure parameters were optimized by the Taguchi method based on the CFD simulations, and obtains the diameters of the upper and lower nozzles of 3 mm and 1.5 mm, respectively; the distance between the upper nozzle and the tool tip of 18.5 mm. To evaluate the cutting and cooling performance of the optimized tool, internal turning experiments were conducted on QT500-7 workpieces. Results show that the optimized tool with internal spray cooling led to lower workpiece surface roughness and chip curling, compared to the conventional tools.

Aiming at the demand of high efficiency green cooling cutting of difficult-to-machine materials, a novel composite cooling turning tool design concept based on circulating internal cooling combined with spray cooling is proposed in this paper. Thermal-fluid-solid coupling simulation models were developed based on FLUENT software, which are applied to investigate the cooling performance of two type of composite cooling tool which are single-nozzle type and double-nozzle type, and the circulating internal cooling tool. Simulations results shown that the tool-chip interface temperatures of the three tools are from low to high under the same conditions, followed by the composite cooling turning tool with double-nozzle, the composite cooling tool with single-nozzle and the circulating internal cooling tool. The double-nozzle type of the composite cooling tool exhibits a better cooling and lubrication performance.

The traditional electromagnetic–thermal bidirectional coupling model (EMTBCM) of permanent magnet synchronous motors (PMSMs) requires a long time to solve, and the temperature-induced torque change is not accounted for in the finite element (FE) numerical calculation of the EM field. This paper presents a precise and efficient EMTBC reduced-order solution model. The specific methods are as follows: First, a torque control technology based on the current injection method is proposed for determining the effect of temperature on the properties of EM materials and EM torque in an EM field, and the accuracy of the FE numerical calculation model is improved. Second, we use the improved EM field finite element numerical calculation model (FEMNCM) to analyze the correlation between the EM loss, the temperature, and the load, and we replace the FEMNCM with the EM field reduction model using the least-squares method. Then, we analyze the law of the PMSM’s internal temperature distribution. We choose the GA-BP algorithm with as few samples as possible and a high accuracy and stability to build the regression prediction model of the temperature field. We use this regression prediction model to replace the complex temperature field calculation. After analyzing the EMTBCM solution strategy, the original complex EMTBC numerical calculation model is substituted with iterations of the magnetic field reduction model and the temperature field regression prediction model. The FE numerical calculation is then used to validate the reduced-order model. The proposed model is validated through numerical simulations. The numerical results indicate that the proposed reduced-order EMTBC model in this paper is accurate and computationally efficient.