Error Analysis And Identification On The Dynamic Performance Factors Of Cnc Machine Tool Based On The "S" Shape Test Specimen

The main effect factors of machining accuracy are the dynamic performance ofCNC machine tools. Dynamic performance testing of multi-axis machine tools is thelack of appropriate evaluation standards at domestic and overseas."S"-shape testspecimens with the open and close angle, the curvature changeable, thin-walled lowstiffness etc geometric features, based on production experience, can reflect thedynamic performance of the machine to a certain extent, and be with some success inthe actual engineering applications. The relationships between the “S” specimen andmachine factors has not yet been set up. The theory of five-axis CNC machine toolsdynamic accuracy is shortageThe paper focus on the "S"-shape test specimens and high-speed five-axis CNCmilling machine, study the geometry and process characteristics of the "S"-shape testspecimens, machining precision evolution and the machine errors traceable method. Themain contents are:1."S"-shape test specimens mathematical model is established. The geometricalproperties and processing characteristics are analyzed with differential geometry theory.The generation mechanism of theoretical error in processing "S"-shape test specimens isanalyzed for the geometric characteristics. The key areas on the "S"-shape testspecimens, reflecting machining performance, are identified.2. Machine spatial error model is to research the basic of dynamic performanceevolution. The feed servo system of the rotational axis and the translational axis areproposed, which is based on MATLAB. The multi-body theory is used to couple axismotion that is integrated into the machine error transformation matrix, the position andattitude of the machine tool center point are described in the absolute coordinate system.The error model is applied to analysis "S" specimen method error mechanism indifferent "S" specimen method error mechanism.3. Fuzzy mathematical theory is applied to set up the traceable model of themachine tool factors, which is success to trace the factors of the greater impact on the machine. Based on the traceable result, the machine factors quantitative identificationmodel is established according to BP neural network theory. The purpose of protectingprecision machining, improving machining performance are achieved4. Based on the orthogonal test design, the cutting "S" specimen test is done. Multiplelinear regression analysis method is applied to isolate single factors influence of the"S"-shape test specimens errors. Based on the obtained experimental results and theRegression analysis conclusion to verify the errors simulation model and the errorstraceability model.