In a flank milling process, the tool rotation profile error induced by its radial dimension error, setup error, tool deflection and wear has a great influence on the dimensional accuracy of the machined components. In this paper, we present an integrated identification of tool error, prediction of machining accuracy and compensation methodology for tool profile error to improve the machining accuracy. Firstly, the tool errors are divided into static and dynamic errors based on the error characteristics and the corresponding error identification methods are established to recognize the tool error parameters. Secondly, the machining accuracy is predicted by a prediction model, and the tool error parameters are input into this model. Thirdly, a new tool error compensation method is developed and incorporated in the corresponding NC codes. Finally, some machining experiments have been carried out to validate the proposed identification-prediction-compensation methodology, and the results show that this methodology is effective.
In five-axis multi-layer flank milling process, the geometric error of tool rotation profile caused by radial dimension error and setup error has great influence on the machining accuracy. In this work, a new comprehensive error prediction model considering the inter-layer interference caused by tool rotation profile error is established, which incorporates a pre-existing prediction model dealing with a variety of errors such as geometric errors of machine tool, workpiece locating errors, and spindle thermal deflection errors. First, a series of tool contact points on the tool swept surface in each single layer without overlapping with others are calculated. Second, the position of the tool contact points on the overlapped layers is updated based on the detection and calculation of inter-layer interferences. Third, all evaluated tool contact points on the final machined surface are available for completing the accuracy prediction of the machined surface. A machining experiment has been carried out to validate this prediction model and the results show the model is effective.
Large aerospace thin-walled structures will produce deformation and vibration in the machining process, which will cause machining error. In this paper, a cutting experimental method based on multi-layer machining is proposed to analyze the influence of cutting tool, cutting path, and cutting parameters on machining error in order to obtain the optimal cutting variables. Firstly, aiming at the situation that the inner surface of the workpiece deviates from the design basis, the laser scanning method is used to obtain the actual shape of the inner surface, and the method of feature alignment is designed to realize the unification of the measurement coordinate system and machining coordinate system. Secondly, a series of cutting experiments are used to obtain the machining errors of wall thickness under different cutting tools, cutting paths, and cutting parameters, and the variation of machining errors is analyzed. Thirdly, a machining error prediction model is established to realize the prediction of machining error, and the multi-objective optimization method is used to optimize the cutting parameters. Finally, a machining test was carried out to validate the proposed cutting experimental method and the optimal cutting parameters.
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