Research On Temperature Field Reconstruction Method Of Heavy-duty CNC Machine Tools

In heavy-duty CNC machine tools,the error caused by thermal deformation is a key factor which affects its machining accuracy.The thermal deformation of the machine tool components during operation will destroy the correct motion relationship and geometric relationship between the cutter and the workpiece,then leads to errors in the machining of the workpiece.Therefore,the change of the temperature field distribution inside the machine tool is one of the main causes of machine tool thermal error.However,in the process of monitoring the temperature field of CNC machine tools,due to factors such as high temperature,vibration,electromagnetic interference,etc.,the sensor is prone to failure,and temperature field data loss often occurs.At the same time,considering the economic cost,machining environment and other factors,the fault sensor is difficult to repair and replace in real time.Therefore,studying the reconstruction method of the temperature field of heavy-duty CNC machine tools is important to saving of the economic cost,analysis of the thermal error and improvement of the machining accuracy of the machine tool.This paper takes the gantry heavy-duty CNC machine tool ZK5540 A as the research object,analyzes the heat source of important parts of the machine tool,realizes temperature field monitoring based on Fiber Bragg Grating(FBG)sensors,and studies the related techniques of recovery of missing data and reconstruction of temperature field.The following is the main research:(1)Study the temperature field monitoring of heavy-duty CNC machine tools.Based on the structural characteristics of the heavy-duty CNC machine tool,combined with the analysis of the principle of FBG temperature sensing,it is proposed to apply FBG sensors to the temperature field monitoring of the machine tool.After analyzing the heat source of the machine tool,the appropriate temperature measurement points are selected,and FBG sensors are arranged to realize the real-time monitoring of the machine temperature field,and compared with the monitoring results of the infrared thermal imager to verify its reliability.Through the analysis of the characteristics of the monitoring temperature field data,it is cleaned by denoising and outlier detection.(2)Study the recovery method of temperature measurement points of heavy-duty CNC machine tools.For the problem of missing values in the monitoring machine temperature field data,a method of recoverying missing temperature data based on compressed sensing is proposed.The method uses K-Singular Value Decomposition(KSVD)algorithm to obtain an over-complete dictionary by adaptively learning the sample temperature data.The gradient alternation iteration method is used to construct the observation matrix,and the Orthogonal Matching Pursuit(OMP)algorithm is used to complete the recovery of missing value.The validity of the proposed method is verified by experiments of missing machine temperature data recovery under different working conditions.(3)Study the reconstruction method of the temperature field of the heavy CNC machine tool.Based on the analysis of temperature field reconstruction principle,a temperature field reconstruction method based on B-spline optimized by particle swarm optimization is proposed.The method uses the Particle Swarm Optimization(PSO)algorithm to optimize node vectors of the B-spline model,and combines the sample data to obtain control vertexs,then obtains the B-spline two-dimensional inverse calculation model to realize the reconstruction of the temperature field.The method is applied to temperature field reconstruction experiments of the heavy-duty CNC machine tool under different working conditions to verify its effectiveness.