Thermal monitoring and thermal deformation prediction for spherical machine tool spindles

Abstract

Machine tool operations and processing can cause temperature changes in various components because of internal and external thermal effects. Thermal deformations caused by thermal effect in machine tools can result in errors in processing size or shape and decrease processing precision. Thus, this paper focuses on the analysis of heating during machine tool spindle's high speed operation, which is the heat source that causes component and structural deformation. In this paper, thermal monitoring was used to build a thermal error prediction model. Temperature change around the spindle was measured with a DS18B20, then multiple regression analysis was used to establish the relationship between thermal deformation quantity and temperature fields at specific points. Finally, finite element analysis was used to build the thermal error model. A solution for the correlation coefficient was obtained using the least squares method. The result of this study verified that finite element analysis can predict front bearing and rear bearing temperature rise, and is consistent with laboratory results. The error in thermal steady-state deformation prediction was less than 2 μm. This information can be used by the controller to effectively compensate the processing and improve processing precision.

Temperature-Sensitive Point Selection and Thermal Error Model Adaptive Update Method of CNC Machine Tools

“…Chen [37] used the multiple regression algorithm to model the Y-and Z-axis thermal error of a vertical machining center, the mean square errors are 3.48 and 4.46 µm, respectively. By comparing the references [33,34] and [23,36,37], it can be inferred that the regression algorithm can achieve the same thermal error modeling accuracy as that of the NN. This is because the relationship between TSPs and thermal error is essentially linear.…”

“…Zhang [23] used sliced inverse regression to model the axial thermal error of a horizontal machine tool, and the fitting accuracy was 2.5 µm. Fu [36] used the multiple regression algorithm to model the axial thermal error of the spindle, and the model prediction accuracy was 2 µm. Chen [37] used the multiple regression algorithm to model the Y-and Z-axis thermal error of a vertical machining center, the mean square errors are 3.48 and 4.46 µm, respectively.…”

Obtaining More Accurate Thermal Boundary Conditions of Machine Tool Spindle Using Response Surface Model Hybrid Artificial Bee Colony Algorithm

“…where f 0 is a factor related to the type of bearing and lubrication method, v 0 is the kinematic viscosity of the lubricant at corresponding temperature, (mm 2 •s), and n is the spindle speed (rpm). The spindle bearing is lubricated by Kyodo Yushi Multemp PS No.2 grease, which has a kinematic viscosity of 16 mm 2 /s at 40 • C. It is worth noting that, due to the different dynamic viscosity of the selected grease, bearing type, and axial size of spindle, the thermal elongation of different spindles vary greatly [16][17][18]. Equation ( 6) is applicable when the spindle speed exceeds 63 rpm and v 0 n ≥ 2000 .…”