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Suppressing thermal deformation is an effective measure to reduce the thermal error of CNC (computer numerical control), and the machining accuracy of CNC directly determines the quality and accuracy of parts. A cooling pipe is designed for the non-heat source parts with large swing head deformation to restrain its thermal deformation. At present, there is a lack of research results in this field. Through the finite element simulation analysis of 5AS01 five-axis direct drive swing head, the thermal deformation of swing head and motorized spindle is compared. It has been determined that the thermal deformation of the shell in the Z- and X-axes has a significant impact on the precision of the swing head's machining; thus, steps must be taken to limit its deformation. The cooling position was determined by simulating the normal working condition of the shell and gray correlation analysis. Capillary copper tube was connected with water-cooling system for cooling, and the heat transfer performance between the shell and copper tube was improved by using heat conductive silica gel sheet. Comparing the shell cooling simulation with the shell cooling experiment, it is found that both of them have the same trend in restraining the thermal deformation of the shell, reducing the thermal deformation in X direction by more than 50% and in Z direction by more than 24%. It verifies the accuracy of the cooling simulation model and the effectiveness of this cooling method, which can effectively reduce the thermal deformation of the shell and improve the machining accuracy of the machine tool. [ABSTRACT FROM AUTHOR]

In the aviation and weapon industry, aluminum alloy thin-walled workpieces are widely used, and milling is a common manufacturing process for these thin-walled workpieces. In milling, many burrs generate on the entrances and exits of workpiece edges and the tops of slot on workpiece surface, which affect machining quality and assembly accuracy and produce more seriously tip discharge effect. To investigate the burr formation mechanism, an analyzed model of top burr thickness considering the tool deflection angle and workpiece deformation is proposed to elaborate the burr formation process in milling of thin-walled workpiece. In this process, top burr formation process is analyzed, and the burr thickness is expressed by the motion relationship between cutting tools and workpieces. Then, based on energy theory, a top burr thickness predicted model considering the tool deflection angle and workpiece deformation in milling of aluminum alloy thin-walled workpiece is proposed. Subsequently, under the determined milling condition, the top burr thicknesses are calculated for verification. Finally, several milling experiments are carried out for validating the feasibility and effectiveness of the proposed model. Experimental results show that the predicted top burr thicknesses are in good agreement with the measured value in milling, and the prediction accuracy of the top burr thickness by the proposed model reached 96.5%. [ABSTRACT FROM AUTHOR]

High-speed motorized spindle is an important component of machine tool, and the influence of heat and deformation generated in the working process on the machining accuracy cannot be ignored. In order to reduce the temperature of the motorized spindle in the machining process, this paper first explores the influence of the flow rate of the motorized spindle cooling water on the cooling water jacket and the overall temperature change. The results indicate that when the flow rate exceeds a certain range, the overall temperature no longer drops, so there is an optimal flow rate in the choice of flow rate. Secondly, to solve the problem of uneven axial temperature distribution of the traditional spiral water jacket, four kinds of cooling water jackets with different structures were proposed. The simulation results show that the difference in temperature distribution inside the serpentine water jacket is small, the overall temperature was reduced by 1.8 °C and the pressure drop was reduced by 62.54 kPa. It shows that the serpentine cooling water jacket has better cooling performance and can effectively reduce the thermal deformation of the motorized spindle shafting, so as to achieve the purpose of improving the machining accuracy. [ABSTRACT FROM AUTHOR]

In order to solve the problem that thermal deformation of CNC machine tools affects machining accuracy, a simulated internal cooling structure based on external copper tube is studied. Method of reducing thermal error of machine tool by cooling temperature sensitive position. Taking 5AS01 double swing angle milling head as the research object, the finite element simulation of temperature field and deformation field is carried out through three-dimensional modeling. It is analyzed that the thermal deformation of the swing head shell in X- and Z-directions has great influence on the thermal error of the motorized spindle, so the cooling of its temperature-sensitive position is studied. The thermal distribution of the shell under normal working conditions was simulated statically, and four temperature-sensitive positions were selected from 10 temperature measuring points by grey relational analysis. The straight capillary copper tube combined with thermal conductive silica gel was used for liquid cooling. Experiments show that the thermal deformation of the shell can be effectively reduced by about 50% by cooling the temperature sensitive points, which provides a reference for the optimization of the structure of the pendulum, and indirectly improves the machining accuracy of the motorized spindle, thus improving the machining accuracy of the machine tool. [ABSTRACT FROM AUTHOR]

High-speed electric spindle is an important part of computer numerical control (CNC) machining equipment. The thermal displacement generated by the electric spindle during operation is the main reason that affects the machining stability and machining accuracy of the electric spindle. Compensating the thermal error of the high-speed electric spindle can effectively improve the CNC machining. Improve equipment processing performance. Therefore, it is particularly important to establish the accuracy of the thermal error prediction model. Taking the A02 high-speed electric spindle as the research object, ANSYS is used to analyze the thermal characteristics of the electric spindle, and the temperature and thermal displacement monitoring points of the electric spindle are arranged according to the simulation results, and the temperature and thermal displacement data of the monitoring points under different rotational speeds are collected; using K-means to classify temperature measurement points, uses the gray relation analysis degree to determine the correlation between the temperature measurement point and the thermal displacement data, and selects 4 temperature-sensitive points from 10 temperature measurement points. Finally, particle swarm optimization (PSO) is used to optimize the penalty factor and kernel function of support vector machine (SVM), and the PSO-SVM prediction model is established to compare with the neural network prediction model of SVM and genetic algorithm (GA) optimized SVM. The results show that PSO-SVM has better robustness, stability, and generalization ability. [ABSTRACT FROM AUTHOR]

The thermal error of the motorized spindle is an essential factor affecting the machining accuracy of high-speed numerically controlled machines. The establishment of a high-speed motorized spindle thermal error model for thermal error compensation can effectively improve the impact of thermal errors on the machining accuracy of the machine tool. This paper proposes a sparrow search algorithm to optimize the Elman neural network to predict thermal errors in motorized spindles. First is the simulation analysis on thermal characteristics of A02 high-speed motorized spindle. Based on the simulation results, the position of the temperature measuring points is arranged in the temperature and thermal error experiment of the motorized spindle. The temperature and thermal displacement data of high-speed motorized spindle at different rotational speeds were collected; secondly, the method of combining pedigree clustering and k-means clustering is used to perform cluster analysis on each temperature measurement point, and the grey correlation degree is used to determine the correlation between temperature measurement points and thermal error. Three temperature-sensitive points were screened from ten temperature measurement points, which reduced the collinearity between temperature measurement points and the number of independent variables of the model. Finally, the weights and thresholds of the Elman neural network are optimized by the sparrow search algorithm, and the thermal error prediction model of motorized spindle based on SSA-Elman neural network is established and compared with Elman neural network and Particle Swarm Optimized Elman Neural Network prediction model. The results show that the SSA-Elman neural network model has the highest prediction accuracy and exhibits good stability and generalization ability. [ABSTRACT FROM AUTHOR]

Marine blue holes are precious geological heritages with high scientific research values. Their physical and chemical characteristics are unique because of the steep-walled structure and isolated water column which create isolated ecosystems in geographically restricted areas. The Sansha Yongle Blue Hole (SYBH) is the world's deepest marine blue hole. Here, we generated the first DNA metabarcoding dataset from SYBH sediment focusing on foraminifera, a group of protists that have colonized various marine environments. We collected sediment samples from SYBH along a depth gradient to characterize the foraminiferal diversity and compared them with the foraminiferal diversity of the costal Jiaozhou Bay (JZB) and the abyssal Northwest Pacific Ocean (NWP). We amplified the SSU rDNA of foraminifera and sequenced them with high-throughput sequencing. The results showed that the foraminiferal assemblages in SYBH were vertically structured in response to the abiotic gradients and diversity was higher than in JZB and NWP. This study illustrates the capacity of foraminifera to colonize hostile environments and shows that blue holes are natural laboratories to explore physiological innovation associated with anoxia. [ABSTRACT FROM AUTHOR]

Supported Mn2O3 is useful in achieving high dinitrogen selectivity at low temperature during ammonia-selective catalytic reduction (SCR). However, its controlled synthesis is challenging when the supporting material is the conventional pure silicon SBA-15 mesoporous molecular sieve. Here we show that silicon and aluminium in fly ash, the solid waste produced by coal-fired power plants, can be used to synthesize an Al-SBA-15 mesoporous molecular sieve support, which can guide the growth of Mn2O3 in the as-synthesized Fe-Mn/Al-SBA-15 NH3-SCR catalyst. Its superior catalytic performance is demonstrated by the high NOx conversion (≥90%) and selectivity (≥86%) at low temperatures (150–300 °C). The combined theoretical and experimental results reveal that the introduction of Al induces the growth of Mn2O3 catalysts. Our findings, therefore, provide a strategy for the rational design of low-temperature NH3-SCR catalysts through dopant-induced component engineering of composite materials. Low-temperature manganese-based denitrification catalysts require a balance between high reduction reactivity and selectivity. Here aluminium in a mesoporous molecular sieve induces the growth of manganese oxide to obtain an iron-manganese catalyst which achieves high efficiency at temperatures of 150–300 °C. [ABSTRACT FROM AUTHOR]

Based on the available experimental data, fiber models for four prefabricated fiber-reinforced concrete beam–column joint specimens with grouted sleeve connections are first developed in OpenSees software. Then, the simulated seismic performance of the specimens is compared with the experimental results. Finally, the effects of axial load ratio and shear-to-span ratio on the seismic performance of the specimens are further investigated numerically. The results indicate that Concrete02 material model and Reinforcing Steel material model can accurately simulate the constitutive relationship of concrete and reinforcing steel, respectively; the beam–column joint elements can accurately simulate different damage behaviors of the joint zone. Fiber-reinforced concrete can significantly improve the seismic performance of the specimens. The relative errors of the simulated seismic performance indexes are about 15%. It is recommended that the optimum value of shear-to-span ratio for prefabricated FRC BCJs is 2.0–2.5. The effect of axial load ratio on the seismic behavior of PBCJs-CM is very small, and can be negligible in the case that the prefabricated FRC BCJs has a moderate value of shear-to-span ratio. The fiber model developed in this article can provide a numerical simulation basis for subsequent studies of prefabricated fiber-reinforced concrete beam–column joint specimens with grouted sleeve connections. [ABSTRACT FROM AUTHOR]