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Author of rapid design system for grille decontamination machine based on UG/NX: Zhou Linzhen and Liu Defang 1 Introduction Grid decontamination machine is a commonly used environmental protection equipment in sewage treatment. Due to the different geographical environments of different users, and even the differences in the geographical environment of the installation locations of multiple grille decontamination machines used by the same user, the design of the grille decontamination machine is a non-standard special machine under roughly the same structure. That is, it is specially designed with general parts designed according to serialization and standardization and special parts designed according to the workpiece, shape and processing technology requirements. It is a one-time design and one-time manufacturing single-piece production product. With the development of the market economy, competition among enterprises has become increasingly fierce. The rapid response ability of enterprises, that is, shorter development and manufacturing cycles, and faster delivery of products to users, has become an important part of the competitiveness of enterprises. The rapid design system for grille decontamination machines based on UG/NX can realize rapid deformation design and sensitive automatic design, so that the design of grille decontamination machines has reliable quality assurance and can best meet the needs of the market and users. On the UGⅢ software platform, this article uses the secondary development tools UG/Open API and Microsoft Visual C++ 6.0 provided by UGⅢ to establish a knowledge base for grille decontamination machine design. It integrates product design knowledge into the product design process, provides reliable technical guarantee for product design quality, shortens the product design cycle, and effectively improves the company's rapid market response and competitiveness. 2. Frame structure and workflow of the system. Based on the design characteristics of the grille decontamination machine, the structure and workflow of the rapid design system for the grille decontamination machine based on UG/NX are shown in Figure 1. As can be seen from the figure, the design of the grille decontamination machine carried out by this system is based on the knowledge base and database, using the acquired knowledge for rapid design. This knowledge is integrated into the product template, including mature grille decontamination machine products, grille decontamination machine design standards, relevant engineering data, expert experience, scientific research conclusions and other forms of knowledge. When designing products, the system is embedded with a design reasoning and design process guidance mechanism. The designer only needs to enter the user's product requirements through the human-machine interface according to the order according to the guidance of the design process, such as channel width, channel depth and other major engineering parameters, and perform simple operations. The inference engine uses these facts to match the premise part of each rule in the knowledge base in sequence, and adds the conclusion part of the rule as a new fact to the product design process. The entire design goes from top to bottom, from product configuration reasoning, overall design, component design to part design, and drives the database to be updated accordingly until the design of all parts of the product is completed. Finally, the assembly form of the product is provided to the designer for design quality review. If the review is successful, engineering drawings, BOM tables and others can be automatically generated. If the designer needs to make partial modifications to the components for this design, they can manually modify the relevant parameters of the corresponding components and re-update the model. ； If the designer hopes that this modification can be used in future designs, he can add or modify the knowledge in the knowledge base through the knowledge manager. The knowledge manager automatically saves the modification to the knowledge base, drives the database to update the model, and ensures that this modification is automatically selected for the next automatic intelligent design. 3 Key technology research 3.1 Mining of design knowledge According to the characteristics and design requirements of the grille decontamination machine, and on the basis of a full understanding of user needs, the acquired knowledge is abstracted, summarized, and organized, and divided into axiom knowledge, design principle knowledge, reasoning and judgment knowledge, product structure knowledge, and design process knowledge according to its source and role in product design. (1) Axiomatic knowledge Axiomatic knowledge includes knowledge related to grille decontamination machines from design standards, manuals, specifications, and examples of mature products at home and abroad. like: Customers' performance requirements for grille decontamination machines, model data of grille decontamination machines, domestic and foreign standards for grille decontamination machine design, etc. It reflects the performance indicators, series specifications and * * Standards etc. (2) Design principle knowledge The design of the grille decontamination machine has hierarchical characteristics. Many design issues of the grille decontamination machine and its components have been summarized into specific mathematical models. For example, the determination of the driving power of the grid decontamination machine, the calculation of the traction force of the tooth rake when underwater, etc. This kind of knowledge is a more precise and important part of the knowledge in the design of grille decontamination machine. We can better grasp this part of knowledge by using mathematical tools and basic design theories. (3) Inference and judgment knowledge Inference and judgment knowledge is the empirical knowledge to solve specific problems in the design process of grille decontamination machine. It is a summary of the knowledge and experience of experts who have been engaged in the design of grille decontamination machine for a long time. For example, based on relevant parameters such as channel depth, installation angle, water depth, etc., determine the number and length of the intermediate guide rail frames, calculate the length of the slag-supporting plate of the grating component, and determine that the length of the slag-supporting plate in the grating component meets the requirements. (4) Product structure knowledge Product structure knowledge mainly reflects the geometric structure and size of parts in the product, as well as the spatial constraint relationship, assembly relationship, and geometric correspondence between parts. For example, the number of channel steels in a beam automatically increases or decreases according to changes in water depth and the average spacing between channel steels. (5) Design process knowledge Design process knowledge reflects the characteristics and requirements of the grille decontamination machine design and controls the entire design process. Mainly include: Product design process, evaluation principles, etc. Such as the design steps of the grille decontamination machine. 3.2 Representation and storage of design knowledge Due to different types, scopes or forms of knowledge, its representation and storage methods in the system are also different. Various types of knowledge that affect the main technical performance of product configuration in axiomatic knowledge are stored in the component file in the form of expressions. For example, the main parameter controlling the configuration of the grid decontamination machine is the channel depth, which is stored in the form of expression B, and the water depth, which is stored in the form of expression H. For axiomatic knowledge that affects the integration of the system and EPR/PDM, it is stored in the main model of the product in the form of features. For example, information such as part materials and specifications are stored in the form of material characteristics and management characteristics. For axiomatic knowledge reflected in * * , industry standards and other types of knowledge are stored in the database in the form of records, such as the correspondence between parts materials, specifications and national standard numbers. The knowledge of product structure knowledge that reflects the geometric characteristics of the model is stored in the prototype model in the form of geometric elements. For example, knowledge related to the shape of the side panels and guide rails of the grille decontamination machine is stored in the form of sketches. The prototype model not only contains information about the structural characteristics of the component itself, but also contains suppressed link geometry that reflects the geometric relationship with other components. This system uses a relational database to store product structure knowledge that reflects the assembly relationships and geometric relationships of parts in the product. For example, knowledge reflecting assembly relationships, geometric relationships, and parameter relationships in product structure knowledge are all stored in relational databases. 3.3 Establishment of the inference engine The inference engine is the core of the system's automatic design. This system uses forward reasoning. After the user enters the product requirements according to the order through the human-machine interface, such as channel width, channel depth, etc., the inference engine will apply these facts to match the premise part of each rule in the knowledge base in sequence. If the match is successful, the conclusion part of the rule will be added as a new fact to the factual product design process. Since product design is carried out from top to bottom, these conclusions will generate new facts as the product design proceeds layer by layer, until the final design of each part of the product is completed. For knowledge stored in the form of expressions, IF-ELSE rules are established, top-down layer-by-layer reasoning is performed, and parameter values in the model are changed, thereby automatically driving the update of the product model. For knowledge stored in geometric form elements, by establishing geometric link relationships between components, top-down layer-by-layer reasoning is performed, and the model shapes of different components are changed, thereby automatically driving the update of the product model. 3.4 Variable structure automatic design. As a typical series of heterogeneous products, the structure of the grille decontamination machine will also change greatly as user requirements change. For example: When the width of the channel is greater than or equal to 2 meters, in order to ensure its strength, the structure of the skimming component will be greatly changed. At the same time, the width of the side plates will also be increased accordingly, and the model of the channel steel that plays a supporting role will also be changed accordingly. To this end, the author uses size-driven technology, constraint-driven technology, geometric object-driven technology and data suppression technology to solve the variable structure design problem of the grille decontamination machine. (1) Dimension-driven technology Dimension-driven technology is mainly used to establish dimensional relationships between components in products. Expressions are an important part of parametric modeling, allowing you to define and control dimensions in your model. Expressions can be used to control the relationship between different features in the same part and the relationship between features in different parts. In this way, dimensional associations within parts and between components can be established. (2) Constraint-driven technology Constraint-driven technology is mainly used to define the positional relationship of standard parts, general parts or general components in products. For example, the positional relationship between the electro-hydraulic push rod and the base in the grille component can be controlled by two alignment constraints and one centering constraint, as shown in Figure 2. Constraint types include: Fit, alignment, angle, parallel, perpendicular, tangent, center, distance, etc. (3) Geometric object driving technology Geometric object driving technology is to connect geometric objects to other components, so that parent components are associated with geometric objects in other components in the assembly. When the geometric object defined in the parent component is changed, the corresponding geometric objects in all components that extract the geometric object will be automatically updated, thereby achieving top-down control of the geometry between components. For example, the outline of the distance plate connected from the grille component is shown in Figure 3. (4) Data suppression technology Data suppression technology controls how components and parts exist in the model by adding expressions that control component or feature suppression in the model, including component suppression technology and feature suppression technology. Component suppression technology mainly solves the problem of component-level structural changes ； Feature suppression technology mainly solves the problem of part-level structural changes. 3.5 Establishment of prototype library This system uses the description method of "prototype = prototype model + prototype data" to classify grille decontamination machines, abstracting a certain type of prototype into a relatively stable model framework, and any specific prototype in this type of prototype is represented as a set of data, that is, the data of any specific instance in the design instance is extracted from the graphics, and system modeling technology or automatic assembly is used to combine the two. In this process, the specific instance is represented as a record in the database. In this way, the query and retrieval of prototype instances are transformed into prototype matching and database management, which simplifies the difficulty of instance reasoning. In the process of establishing a prototype model, product structure knowledge that reflects the internal geometric structure and parameter relationships of the part can be achieved through parametric modeling technology. For example, for nuts in national standard parts, a three-dimensional parametric prototype model related only to the nominal diameter can be established through parametric modeling technology. However, product structure knowledge that reflects the geometric and parameter relationships between parts cannot be achieved simply through parametric modeling technology. Product structure knowledge that reflects the geometric and parameter relationships between parts must use system modeling technology to establish prototype models. 3.6 Automatic update of prototype model This system uses assembly cloning technology to clone the prototype model and load the three-dimensional model generated by the clone into the product model. The 3D model generated by cloning inherits all the information of the prototype model, so that the 3D model loaded into the product model contains product structure knowledge that reflects the design intent and geometric relationships between parts. Although the three-dimensional model that is cloned and loaded into the product model contains product structure knowledge that reflects the geometric relationships between parts, the geometry that reflects these product structure knowledge is in an interrupted state. This system re-links the interrupted link geometry by restoring the associated features, that is, restoring the geometric relationship between the child model and the parent model, and realizing the geometric control of the newly loaded three-dimensional model by the product control structure. Since the product control structure is customized according to the requirements of the engineering parameters input by the user, the newly loaded model will be automatically updated according to the engineering parameters input by the user. 3.7 Automatic assembly of the prototype model The automatic update of the prototype model enables the geometric relationships and parameter relationships of the newly added 3D model to the product model to meet the user's engineering requirements. However, the assembly relationship of the newly added 3D model must also be automatically assembled based on the knowledge in the assembly knowledge base. The main problems solved in the automatic assembly of prototype models are:: The realization of different assembly relationships of the same parts. For example, there are a large number of the same standard parts in the grille decontamination machine, but the assembly positions of these standard parts are not the same. ； Realization of assembly relationship between parent and child models ； Implementation of assembly relationships between assembly models. This system adds component name information to the assembly knowledge base. The file name corresponding to the model reflects whether the loaded model is unique. The component name corresponding to the model indicates the name of the model in the assembly after loading. The file name + component name is used to distinguish assembly issues of parts with the same name. When establishing the prototype model, the assembly relationship between the parent and child models is represented by the link geometry, so that the assembly relationship between the parent and child models is automatically updated when the prototype model restores the link feature information. For the assembly relationship between assembly models, the geometry reflecting the assembly relationship of the models is promoted to the assembly model through feature promotion, and then the promotion features in the assembly model are used to realize automatic assembly. 3.8 User interface design The user interface mainly provides an interface for designers to input design parameters to complete the design. UGⅢ provides a visual dialog box editing environment. Users can easily customize the design interface according to product design requirements. * * The development cycle of the product design system is shortened. Figures 4 and 5 show the design interface of the rapid design system and the three-dimensional digital model of the grille decontamination machine respectively. 4 Conclusion This paper organically combines the practical technology of knowledge engineering with the overall product parametric design technology on the UG III software platform, and develops an engineering, practical, and knowledge-based digital rapid design system for grille decontamination machines based on UG/NX. This system analyzes the user needs of the grille decontamination machine, extracts relevant knowledge about the design and variant design of the grille decontamination machine, and stores it in the knowledge base. It establishes a prototype library that integrates system modeling technology and establishes a multi-layer reasoning mechanism based on prototype reasoning. It can provide rapid support for the entire process of grille decontamination machine design. It allows users to input a small number of engineering parameters through intelligent guidance, perform appropriate human-computer interaction to drive the design process, and automatically generate a digital prototype of the grille decontamination machine that meets the individual needs of customers. The next stage will further study the development rules and technologies of the system knowledge base and inference engine, and integrate CAE technology into the system to raise the product design level from empirical design to optimized design. References: Jing Bingbin, Zhang Jingmin. Market needs and their impact on product design. Mechanical Design and Research, 1998 (1): 15-17 Wang Ping, Zhou Yimin, Liu Defang. Knowledge-based rapid design system for grille decontamination machines. Mechanical Manufacturing and Automation, 2002, (6):76-78 Wu Weidong, Liu Defang. Research and development of rapid product design system based on model reasoning. Journal of Beijing University of Technology, 2003, (2): 155-158 Zhou Yimin, Wang Ping, Liu Defang. Application of KBE technology in the design of grille decontamination machine, mechanics and electronics. 2003, (2): 38-40 (end)