Guoqiang Fu

Error compensation of free-form surface with critical area based on T-spline surface reconstruction

In this study, a novel method for error compensation of free-form surface with critical area is investigated. With the proposed method, the machining accuracy of the surface can vary from area to area according to the precision requirement. The local refinement of the T-spline surface is used to set more sampling points in the critical area. A compensate surface is constructed according to the on-machine measurement data to improve the machining accuracy of the surface. Simulation result shows that the T-spline surface has better fitting accuracy compared with the NURBS surface, given the same coordinates information of the surface. Verification experiments showed that the mean machining error of the normal area is improved by 62.1%, while the mean machining error of the critical area is improved by 73.9%.

A polygons Boolean operations-based adaptive slicing with sliced data for additive manufacturing

In order to increase the efficiency of additive manufacturing, this paper proposes a novel adaptive slicing approach of sliced data with minimum thickness based on Boolean operations of polygons. It can greatly handle the balance between the build time and the surface precision of additive manufacturing. The proposed adaptive slicing is available for the single solid model, the support of additive manufacturing, and simultaneously manufactured multiple models. At first, the Boolean operations of polygons are used to gain the relationship of the adjacent layers to serve as the topological information. Second, two parameters are proposed to evaluate the precision of sliced surface: the ameliorative area ratio and variation of the cusp height. Ameliorative area ratio overcomes the drawbacks of original area deviation ration criteria and can work on the large and complex models. Variation of the cusp height makes the calculation of cusp height suitable for sliced data of model, and it is independent of the normal vector of surfaces. Third, the adaptive slicing is realized by removing unnecessary layers based on two parameters and the maximum allowable thickness. The thicknesses are times of the minimum thickness. Moreover, the adaptive slicing for support of additive manufacturing is developed through dividing the support into two parts according to its height and location. Slicing of multiple models is also proposed by choosing the maximum ameliorative area ratio and variation of the cusp height among all models in the same z level as the two parameters. Finally, the adaptive slicing for the three types is tested with some special models, and corresponding models are printed with FDM technology based on slicing results of the proposed approach. Results show that the proposed adaptive slicing approach is effective.

The tool following function-based identification approach for all geometric errors of rotary axes using ballbar

This paper proposes a tool following function-based identification approach (TFFIA) for geometric errors of two rotary axes for one five-axis machine tool. It is comprehensive to identify all geometric errors of rotary errors. Firstly, synthetic error formulas of ballbar originate from the geometric error model of machine tools in order to consider the influences of 21 errors of three translational axes. It makes the approach more reasonable and precise. Secondly, the structures of three measurement patterns of TFFIA are described. Thirdly, in each pattern, errors of rotary axes affecting the accuracy of the sensitive direction are identified. As the result, the identification equations of all 20 errors coincide with the geometric properties of errors. Moreover, the impacts of setup errors of ballbar are eliminated with least square method to improve the precise of TFFIA. According to the structures of three patterns, only three installation of workpiece ball of ballbar are needed in the whole identification of two rotary axes to obtain the required ballbar readings. It greatly shortens the measurement time. Twenty geometric errors of two rotary axes are calculated with identification equations and ballbar readings. Finally, TFFIA is applied to a SmartCNC500 five-axis vertical machining center. The corresponding comparisons are proposed to verify the effectiveness and accuracy of TFFIA.