Research On Thermal Characteristic Analysis And Modeling Of High Speed Motorized Spindle

With the rapid development of modern manufacturing industry,high-speed precision machining technology has become an important development trend of current machining.High speed and high precision CNC machine tools are prerequisite for high-speed precision machining.Motorized spindle is the core component of CNC machine tool,which is the key to achieve precision machining.Due to the compact structure of high-speed motorized spindle,the heat generated by the heat source is difficult to dissipate,which will cause thermal deformation,resulting in reduced performance.At the same time,the thermal error caused by motorized spindle is the largest error source of the machine tool,which has a great influence on the machining accuracy of the machine tool.In order to improve the performance of motorized spindles,an in-depth analysis of their thermal characteristics and thermal errors is required.In this thesis,the A02 high speed motorized spindle is taken as the research object,and the thermal characteristics analysis and thermal error modeling of the motorized spindle are studied by theoretical derivation,simulation analysis,experimental measurement,and model construction.Firstly,analyzes the structure of A02 high-speed motorized spindle,and calculates its thermal load and convective heat transfer coefficient based on the theory of heat transfer,and uses it as the boundary condition of finite element model to establish the thermal-solid coupling model of motorized spindle.Through in-depth analysis of the heat generation and heat transfer mechanisms of motorized spindles through steady state and transient states provides a theoretical basis for the study of the thermal properties of motorized spindles.Secondly,set up motorized spindle temperature and thermal displacement data acquisition test bench.A complete thermal error acquisition system is established through sensors,data acquisition instruments and supporting software.Through the design of the working condition scheme of the motorized spindle,the temperature and thermal displacement data of the motorized spindle at different speeds are collected,and data support is provided for subsequent research.Subsequently,temperature sensitive points are screened based on the collected temperature and thermal error data.The clustering analysis of each temperature measuring point was carried out by using the combination of pedigree clustering and K-means clustering,and the clustering results were evaluated by Silhouette function.At the same time,the degree of correlation between temperature measuring points and thermal displacement is discussed by grey correlation analysis.The resulting screening of temperature sensitive points with low collinearity between temperature measuring points and high correlation with thermal displacement are screened.Screening based on temperature sensitive points can provide input for thermal error model and improve the performance of the model.Finally,combined with neural network and optimization algorithm,the weights and thresholds of the Elman neural network are optimized by using the beetle antennae algorithm and the sparrow algorithm respectively.The selected temperature sensitive points are used as input,and the axial thermal displacement is used as output.The thermal error prediction models of BAS-Elman and SSA-Elman motorized spindles are established,and the accuracy and generalization of the proposed models are verified by experimental data at different speeds,which provides a new method for thermal error modeling.