Regular patterns of repeated elements in CAD assembly model retrieval

Abstract

While machining width is an important factor of the machining time of freeform surface finishing operations, in reality the kinematic capability of the machine tool is usually the bottleneck of achieving higher feed speed and optimal machining time. The purpose of this paper is to conveniently (and approximately) determine the optimal cut direction considering the speed kinematic capability of the machine tool, without having to compute the actual tool path. We propose a mathematical instrument, called Machine Kinematic Metric (MKM), to easily evaluate infinitesimal machining time on a freeform surface based on machine kinematic consideration. It's a tensor field similar to the metric tensor in differential geometry. MKM is integrated over the part surface to approximate the cut-direction-dependent total machining time, and used to determine the optimal cut direction that minimizes the machining time. To validate the accuracy of the prediction using MKM, we apply the method and compute the machining time at every direction with one degree apart and derive the optimal cut-direction. The computation is performed on two examples: a simple freeform surface and a complex die face model. We then use a commercial CNC emulator software from Huazhong CNC to precisely simulate the machining time in distributed cut directions (five degree apart) for the two models. We find that the optimal cut direction determined from CNC simulation is consistent with the prediction from the proposed method. It validates that the proposed method is a convenient and economical tool to approximately determine the optimal cut direction based on machine speed kinematic capability.

Content-based CAD assembly model retrieval: Survey and future challenges

“…The considered patterns collect the maximum number of repeated parts whose barycenters are equidistant and lying on the same linear and circular paths. To overcome the possible absence in CAD models of the information on component type and on patterns, the authors provided tools for their extraction [84][85][86]. The shape layer describes parts in terms of their 3D spherical harmonics [68], their volume and their surface area, which scale are sensitive (✓).…”

An Efficient Assembly Retrieval Approach based on Compressed Part Parameter Vector

“…A heuristic algorithm based on simulated annealing was used to solve this problem, and the similarity of the assembly models was evaluated from multiple perspectives. In addition, Katia et al introduced an approach for automatically extracting component relationships in assembly models [34][35][36] , and Matteo et al introduced a machine learning-based method for classifying parts 37 .…”

Generic Structure Construction of 3D Assembly Model Based on Conjugate Subgraph

“…Definition (1): Attributed Adjacency Graph (AAG). It mainly takes the part face as the node and the adjacency relationship between faces as the edge of the graph representation model to represent the topological relationship of the part, which is often expressed by G � {V, E, α, β}, wherein V represents the set of nodes, and any element v i in the set meets the corresponding relationship with one side in the part; E represents the set of edges, which is the adjacency relationship of faces, and any element in the set has an element in V corresponding to it, mainly including the geometric type of faces, the number of edges of faces, and so on; and β represents the attribute set of edges, and any element in the set has an element in E corresponding to it, mainly including the type of edges and the position relationship of adjacent faces.…”

“…3D assembly model contains much reusable information re ecting the design intention, such as structure, function, and attributes. In the design and manufacturing of products, scholars at home and abroad have done a lot of research on the mining and reuse of relevant information for CAD models [1][2][3][4][5][6][7][8]. In the process of information reuse, designers often pay more attention to local similarity comparison.…”