Research And Apply On Intelligent Design Key Technologies For Fixture Domain

It's very important for manufacturing system to improve the quality and efficiency of fixture design.Computer Aided Fixture Design (CAFD) is effective to solve this problem, but the CAFD system based on CAD is mainly focus on the modeling of fixture. There exist some problems in the approach of fixture design, although the intelligent method is used. The geometry information is ignored in most of these systems, which make it difficult to realize the intelligent fixture structure design. On the other hand, the efficiency case-retrieval and case-reuse is limited without geometry information in case based fixture design, because the fixture structure mainly depends on the topology of workpiece. In the end, the expert system used in intelligent fixture design cannot extract knowledge, which confines the use of the intelligent system to some extent.A concept of general geometry reasoning is proposed and used in intelligent fixture design based on feature recognition, 3D model retrieval and traditional geometry reasoning in this dissertation. Extracting the geometry information from 3D models, geometry reasoning, representation of 3D models, the similarity of CAD models with engineering semantics, the intelligent variant design based on geometry similarity and the extraction of fixture design are the main direction of the dissertation. A CAFD system is developed based on these theories. The main innovation as follows:Fixturing feature recognition is proposed based on the attributes ajoined graph.The recognition method of terminal faces is proposed based on classify of all concave edge connected face, and the direction of manufacturing feature is defined. The complete algorithm of fixturing feature recognition is presented based on this method, which is the basis of automotive design of fixture. The feature recognition based on double-link genetic algorithm is proposed for the special feature request in the fixture design such as T-slot, which can overcome the shortcomings of the traditional feature recognition.The completed fixture planning algorithm faced to the 3D fixture design is presented based on the Brost-Goldberg Algorithm.The classic Brost-Goldberg mainly focus on the planning of the fixture of 2D parts or prismy parts. This algorithm is combined with the feature recognition in this dissertation and extended to the 3D fixture design. The clamping planning is discussed too.The complete description of fixture parts is proposed. The concept of assembly feature loop is described and used in the detail design of fixture. The automotive design of fixture is extended based on these methods.An attention-driven model of workpiece is proposed to fit the case retrieval of fixture design for overcoming the shortcoming of the case retrieval based on properties.The algorithm to get the attention-driven model is described in detail. The algorithm of similarity matching based on the attention-driven models of parts is described in detail.A new retrieval approach of fixture case is discussed based on the attention-driven model, which can improve the efficiency of the case retrieval. A new method of intelligent case revise is presented based on the similarity of feature, which can solve the problem of low level of case revise in case based reasoning.A new machining learning approach is provided based on geometry structure data-mining and Bayesian network. The description of the fixture structure is presented based on the graph data structure and the algorithm to get it is proposed too. Then the clustering algorithm of fixture is introduced detailedly. The extended attributes adjoined graph is presented to describe the topology of part, and the support graph of fixture structure is defined too.The learning method of the support graph is desicribed in the following sections. The Bayesian network is used to learn the contaction between the technical attributes and the fixture structure.The reasoning process using the learned knowledge is depicted at last.