| The processing stability of machine tool is directly influenced by the dynamic characteristics, and then influences some critical parameters such as processing efficiency, workpiece quality and the tool life. The stability lobe diagram is commonly used to predict stable sets of machining parameters. To obtain the stability lobe diagrams, the frequency response(FRF) of machine tool at the tool tip is an essential requirement.To eliminating repetitive FRF tests for each tool, a receptance coupling technique was established to build the prediction model for the FRF of machine tool at the tool tip. The machine tool syste m was divided into three substructures: spring- holder(upper section), holder(under section) and tool. The dynamics of each substructure were identified separately and combined using the receptance coupling technique to obtain the assembly dynamics. A complex stiffness matrix computation method was used to identify the flexible joint. The flexible joint was represented by an 2-by-2 symmetric stiffness matrix. The three unknown elements of the matrix were solved by IRCSA method. So the FRF of different holder-tool combination at tooltip could be predicted. Two flexible joint identification methods, namely the complex stiffness matrix computation method and the genetic algorithm method, were compared in its efficiency and accuracy. The complex stiffness matrix computation method had more accurate mod el and higher computing efficiency but it's more sensitive to testing error.Five dynamic characteristics evaluation parameters were selected in the thesis to evaluate the dynamic characteristics of machine tool, namely static stiffness, natural frequency, the minimum dynamic stiffness, weakly rigid bandwidth, overlarge flexibility. The way to obtain the dynamic characteristics from the FRF was given in detail. A relevant multi-axis machine tool dynamic characteristics prediction and evaluation software was developed by integrating the computation work by MATLAB and the modeling and analysis work by ANSYS all together based on the theory and algorithm mentioned above. The overall framework of the software was established with the tree structure. The RCSA method and the evaluation parameter extraction method were simplified into the specified input and output operation of the software, such as the input of measured data and holder model and the output of the predicted FRF. The software could be used to predict and evaluate the dynamic characteristics of the machine tool of different holder-tool combination at tooltip under different work position.The theoretical method and the evaluation software above was respectively applied to test, predict and evaluate the dynamic characteristics of a planer milling machine and an orthogonal turn-milling machine under different work position. The quantitative evaluation of the machine tool dynamic characteristics was generated. The stability lobe diagrams were obtained based on the dynamic evaluation to analyze the stability of the machine tool. |
PDF Full Text Request