The Intelligent Dimension Study Of Combined Entity Oriented MBD

The development of CAD/CAM technology is promoting the designing and manufacturing of mechanical products toward to be more intelligent, integrated and flexible. Thus the traditional designing and manufacturing method which mainly depends on 2D drawings and 3D entities will be replaced by the 3D digital definition mode.Model Based Definition designing method has been used more and more widely, it considers the 3D entity as the only date source so that the integration of manufacturing information can be achieved. 3D dimensioning as an important part of digital definition, due to the multiplicity and flexibility of entities, the automation and intelligence of 3D dimensioning has been studied as an important issue in the field of digitization.The combined entity is defined as an entity that formed by a combination of a number of basic geometric entities in a certain order and mode. When its feature type and spatial topological relations are determined, the numbers of its 3D dimensions are consequently set. The numbers of dimensions of a combined entity needed to mark at different benchmarks are same. According to the certainty and uniqueness of 3D dimensioning of combined entity, this master degree thesis introduces the idea of decentralization to solve the problem of automatically generation and adjustment of dimensions.The introduction of digitalization development history and the illustration of necessity and feasibility of research on 3D digital intelligent dimension are presented in chapter one. The combined entity was selected as research objective, and CAA was employed as the platform for programming the intelligent 3D dimensioning.The feature of entity was described by hybrid representations and its parameters were extracted by three dimension decomposition method in chapter two. The basic geometric entities were abstracted by a research method based on an object oriented approach. In addition, the semantic definitions of the function of basic geometric entities were illustrated.The relationship between the degree of freedom of feature and constraint is explained in chapter three. Internal constraint and external constraint of basic geometric entity were respectively related to the shaping dimension and location dimension. This chapter clarified that that the solution of constraint means the determination of dimension, and directed constraint network for propagation of dimension constraint was made. The directed constraint network of dimension refer the relevance between every dimensions, it is the foundation to achieve the intelligent dimensioning.In chapter 4, the benchmark selection of dimensioning was determined. Then the generation and adjustment of shaping dimension and location dimension were studied. The shaping dimension part mainly introduces the determination method of lost dimensions, and this method was illustrated by examples. The locating dimension part mainly introduces the decomposition of nonlinear dimension and annotation of symmetrical dimension.The introduction of six modules for the frame of intelligent dimensioning system is presented in chapter 5. The six modules include the basic geometric entity feature recognition, extracting the feature information, generation and adjustment of shaping and location dimension, adjustment and annotation of dimension. A combined entity is used as an example to explain how these six modules working.In chapter 6, the research contents and methods in this thesis are summarized. Besides, there is a future scope for the future direction of this subject, and proposed the appropriate adjustments to future research directions.