Many production-oriented enterprises are confronted with the same market demand currently, that is, varieties of products and small batch as well. How to identify, classify and manage those numerous different parts quickly and automatically? It is the critical task for many companies to improve products classification efficiency and their qualities with high efficiency.In order to ameliorate the low efficiency and less precision of manual inspection and classification in some production process, the thesis puts forward a new kind of identification, classification and management techniques for modern industrial products, which contains a set of machine vision, automatic control, radio frequency technology, manufacturing execution systems, and so on.Based on our previous flexible manufacturing system (FMS), related key technologies, such as system design, software development and production identification and classification management are researched in the paper.The following key technologies are involved in the research work:machine vision technology. RFID technology, motion control technology, and information management technology. Based on the analysis of the basic principles, status and trends of the above technologies, an identification and classification management system for industrial products is studied.On the basis of analysis on the system architecture, its functional requirements and system design goals, manufacturing executive system (MES) is designed and implemented. Some technologies, including machine vision and RFID, are involved in MES, which contains hardware, software and network architecture.As for MES data acquisition and processing, several different ways of the existing data acquisition are discussed. Information storage and data acquisition automatically are introduced. Furthermore, software is developed with VC++6.0and ACCESS database via Activex Data Objects (ADO).Identification and classification management system has been executed and verified in flexible manufacturing system, and the test data is analysed.The main work and key points are summarized, and the further research work is proposed as well. |