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2 Recognition and mapping of 3D solid features
2.1 Identification of 3D solid features
Feature recognition and extraction is the first and most important step in the design of model data transformation to manufacturing instructions. Differentiating from the direct integration of the CAD platform, the feature recognition method can be divided into the integration based on the CAD graphic expression model and the integration based on the CAD semantic expression model. Feature recognition based on semantic expression model generally uses GT coding and descriptive language symbols to input part design information. This design model is generally established by hand, with high work intensity and low degree of automation, and cannot fully utilize the output model of the design system. Feature recognition based on CAD model is an effective way to integrate CAX system.
The currently used CSG (Constructive solid geometry) and B-rep (Boundary representation) entity representation methods represent the solid model composition of most CAD platforms. The research on the feature recognition methods of these expressions is the main focus of current feature recognition research, and considerable progress has been made.
These methods generally only deal with pre-defined features. If a feature is not defined beforehand, the result of feature recognition must have omissions and defects. On the one hand, it is necessary to establish a general feature library to pre-define general features by means of induction and arrangement; on the other hand, it is necessary to customize the feature library for product objects and manufacturing resources. And study the robust feature recognition algorithm. The feature extraction is solved from the aspects of commonality and personality, algorithm basis and so on.
The paper focuses on the volume-based spatial decomposition algorithm. The principle of the algorithm is to separately decompose and compare the space volume occupied by the part solid model and the feature. If a part of the part body covers the feature volume space, the volume is extracted. In order to simplify the recognition algorithm and speed up the algorithm, the decomposition and recognition algorithm of feature volume based on graph Boolean operation is proposed.
2.2 Mapping and transformation of features
Since the machining process and the design process are two interrelated phases, there is a mapping relationship between the design features and the process features. The mapping of features is based on a predefined library of process features, and the reasoning at this stage is reasoning based on semantic notation.
The process characteristics are directly related to the processing and manufacturing methods. In the general plastic forming (such as forging sheet metal) and cutting forming (such as turning and milling), each processing method corresponds to the machinable design. feature. This processable feature is defined as a process feature; the feature for positioning and clamping is defined as a clamping feature. Design features and process features are coincident in some cases; in some cases, such mapping is more complicated. Considering other types of features in the design features other than geometric features, such as accuracy, material, mechanical properties, etc., other mapping relationships between design features and process features may result.
For these mapping relationships, the solution adopted here is: firstly define a relatively complete process feature library, abstract the relationship between design features and process features and fixture features in a regular form, and implement feature mapping process based on rule-based reasoning methods. Of course, a high degree of intelligent target may be at the cost of increasing the difficulty of the entire system. For this reason, intelligent mapping is used to assist the designer's manual conversion in the system implementation to finally achieve the feature mapping.
3 Feature-based process reasoning
3.1 Feature-based process inference technology
The process characteristics of the part and the relationship between the features reflect the process characteristics of the part. Both the process design and the tooling design need to infer the processing methods and process contents required for processing from the process characteristics. This process is closely related to manufacturing resources while embodying experience. Derivative or generative process design is the most basic process. Rule-based reasoning is mainly used for the selection of processing methods and process contents. Case-based reasoning, frame-based reasoning, and object-oriented methods are used in combination with rule-based reasoning. This hybrid reasoning method is used to solve the problem of insufficient rule-based reasoning ability that may result from incomplete knowledge acquisition. Techniques such as fuzzy reasoning and artificial neural networks can be used to sequence process flows; genetic algorithms are used to optimize a set of feasible process plans.
3.2 Intelligent reasoning model
In this paper, a method based on case-based reasoning and rule-based reasoning is used to realize the process derivation process. The example retrieval process uses a comprehensive matching of product design, process specification and fixture design at the two levels of part and feature level. When the closest instance is obtained, the difference between the instance and the target instance is first compared. This comparison process is a comparison of the incomplete traversal of the two tree data structures. The result of the comparison will be used as a premise of rule reasoning to further obtain a more complete solution. The combination of fuzzy evaluation and genetic algorithm is used to evaluate and optimize the inference results.
3.3 Organization and expression of knowledge
There is a coincident attribute between the instance expression model and the part information model. The part information model provides basic properties of the instance based on instance reasoning. The article is based on the part information model; additional information is added to form an example of process reasoning. Additional content includes knowledge of boot instance adjustments and modifications, primarily summarized and saved in the form of rules. The contents of the rule are not saved in the instance library, but only the references to the rules in the rule base.
4 system overall frame design
Unified analysis and design of functional or overlapping parts of CAPP and CAFD systems. At the same time, the individualized functions of the two systems are partially designed independently. With a flexible component structure, you can build independent CAPP and CAFD systems, as well as an integrated, unified CAPP/CAFD integrated system. This design and implementation ensures system integration, openness, reconfigurability, and reusability of components.
4.1 Overall framework
The overall functional model of the system can be divided into five sub-models: product information model, process reasoning model, fixture design model, manufacturing resource model and comprehensive constraint model. The interaction of these five sub-models constitutes the overall function of the system, as shown in Figure 2. The product reasoning sub-model is the key to system function modeling. The static functional structure of the sub-model established by object-oriented technology and UML method is shown in Fig. 3.