State Prediction Model of Five-axis Machine Tools based on the "S" Test Piece Surface Finish

Abstract

With the development of industries and advanced manufacturing technology, the performance of the machine tools plays an important role for the product quality. Nowadays five-axis linkage machining centers (5-axis MC) have been applied in the aerospace industry widely. During the thin-walled aircraft parts machining process of the 5-axis MC, the stochastic chatter phenomenon has important influence on the machining precision and quality of the parts. It has been the research hot point due to predict the 5-axis MC state hard with the complex operating process. “S” test work-piece(S test piece) is with the features of aerospace thin-walled parts. A State Prediction model for 5-axis MC based on the S surface finish is presented. Firstly, the movement principle of 5-axis MC during the S test piece processing is analyzed by using VERICUT Simulation tool; Then the information coupling method during geometry, operating process, monitoring and S test piece surface finish is built based on external export directions. By using the Geometry-Process-Monitor-Surface Quality information coupling method, the monitoring signal information data included vibration signals and acoustic emission signals are obtained while a S test piece finishing processing; the 5-axis MC state is predicted by using the ensemble empirical mode decomposition(EEMD) method with three indicators: fractal dimension (FD ),standard deviation (SD)and Power spectral entropy (PSE). By using a coordinate measuring machine (CMM) and a Scanning Electron Microscope to measure the machined S test piece, the actual geometry and surface finish of the S test piece can be obtained. Finally, the mapping relationship during the geometry-operating process-monitoring-surface finish of a S test piece are constructed. The prediction system is developed by using LABVIEW and MATLAB. This model is applied to the 5-axis MC and the experimental results show that this model will be the effective means for predicting the state of the five-axis machining center.