Study On Intelligent Partitioning Of Large-Scale Models For Rapid Prototype Manufacturing

Model partitioning of rapid prototyping (RP) makes it possible to fabricate arbitrary large-scale models which are larger than the maximum dimension of the current RP machines. For the large-scale rapid prototype manufacturing, the objective of model partitioning is not just dividing a large model into a set of small sub-model, but decomposing the large complex model into several sub-models which are simple, moderate size and easy to fabricate and assembly.Aiming at the data redundancy and no topology information of STL model that is the common data interface of RP system, the shape representation and data structure of STL file are analyzed. The Hash-table algorithm was used to merge the vertices of the model. The Forward edge structure was proposed to reconstruct the data topology of the model. The model denoising based on two-sided filter was used to improve the quality of the model surface.Aiming at the complex clustering and iteration of the vertex-based curvature estimation, the edge-based curvature estimation was proposed to extract the feature edge. The calculation of the edge-based method is simpler than the vertex-based method. The three curvature parameters are dihedral angle, perimeter ration and convexity. The genetic algorithm (GA) was used to determinate the threshold of feature edge. The fitness function is the maximum variance of classes (MVC).Aiming at the discreteness of feature edges in the edge chain, the best-fit plane (BFP) was proposed to group and link feature edges. The similar and adjacent feature edges were firstly grouped to be feature edge sets. The BFP of a feature edge set was calculated by using the least square method (LSM). The isolated feature edges can be grouped or deleted based on BFP. The Dijkstra's algorithm was used to close the incomplete feature boundaries.Aiming at the requirement of manual partitioning, the interactive partition method was researched. The interactive operation of STL model was achieved in the OpenGL environment. The partition positions (vertex, edge, facet, or scoring) can be selected by the operator using the mouse. The decision tree of cutting boundaries was constructed according to the relative position and similarity of the partition positions and the feature loops. Based on the inductive learing (IL), the optimal cutting boundaries were created on the partition positions for assisting the manual partitioning. Aiming at the requirement of automatic partitioning, the intelligent partition method was researched. Generally, the large complex model has a lot of partition schemes. It is different to select the best scheme by the enumeration method. The multiobjective optimization (MOO) algorithm was proposed to select the best partition scheme. On condition that all sub-models should be smaller than the maximum dimension of the RP machine, cutting times, complexity of sub-models, utilization ratio of materials and planarity of cutting boundaries were synthetically considered. GA was also used to generate the optimal partition scheme for guiding the automatic partitioning.Aiming at the effective construction of assembly features on the sub-models, the similar-shaped assembly feature was researched. According to the outside cutting contour, the similar-shaped contour was constructed by using the Voronoi diagram algorithm. The maximum assembly depth was calculated by using the projection method. Based on the similar-shaped contour and the maximum assembly depth, the similar-shaped assembly features were automatically created for facilitating the assembly of sub-models.Based on the above research of model partitioning and assembly feature, a prototype system of model partition and assembly feature construction for large-scale rapid prototype manufacturing was developed by using Visual C++. More than ten experimental models were used to validate the validity and generality of the proposed algorithms and the developed system.