Research And Implementation Of An Intelligent System For Material Selection

The Modern Design Theory holds that decisions made during the design period determine70%of the product's structure, function and cost. Material selection is an extremely important part of the design of mechanical products. In recent years, many new materials are adopted to reduce weight, improve performance and reduce the costs. A lot of major mechanical failure is not because of the kinematic design and dynamic design, but because of the inappropriate choice of material types and grades. With the emergence of a large number of new materials, the innovative design material selection aspects of innovation to support. Therefore, how to choose the best materials in a huge number of materials has become the research hotspot.This paper research and develop an intelligent material selection system; establish a materials database and a knowledge base and focused on the application of BP neural network and expert system for material selection. This work includes the following aspects:1. We discussed the problems in the traditional method of material selection and analyzed the status of research on material selection methods and expert systems of domestic and international and introduced the theoretical knowledge of neural networks and expert systems. After analyzing the advantages and disadvantages of neural networks and expert systems, we developed a material selection system applying a material-selection strategy combined BP neural network and expert system to select the suitable material for designer.2. Found material data sources; Researched the standard storage format of the material; established a materials database that contains the information of most engineering materials and developed the material query module. We established a knowledge base. The system obtains the material selection knowledge of experts through learning sample and summarizing and stored the knowledge in the knowledge base.3. Developed the material selection module with the knowledge stored in the knowledge base, which contains a gear selection BP neural network and a decision tree. The system selects the suitable material for designer with the form of questions and be able to make the reasonable explanation of the results.4. Developed the knowledge management module, the user feedback module, the help module and the user management module to improve the function of the system. The system adopts B/S structure and all services on the server in order to reduced the load of client. Users can use the system easily and comfortably.5. The system's effectiveness was demonstrated by a material-selection case in actual applications. This system could help designer select suitable materials and provide knowledge for inexperienced engineer. So it has great application significance.