Research And Application On Control Technology Of Knowledge Flow In Networked Manufacturing

The communion and sharing of knowledge is an important precondition to innovate. Innovation needs the flow and collision of knowledge. The communion and sharing of knowledge is gradually becoming a basic mode for the interaction among groups and individuals, an important approach to improve performance. In networked manufacturing, the sharing of knowledge is an important characteristic, which promotes knowledge to flow among enterprises and individuals. The flow is not aimless, but orderly through control. Based on networked manufacturing, the method of knowledge modeling, the property of knowledge flow in resource space model, the controlling model and the controlling strategy of knowledge flow, the integrating control between knowledge flow and workflow are studied. Furthermore, the new method and technology were applied to networked manufacturing of enterprise through practical project, and good results were attained.The main study of this dissertation is as follows:In chapter 1, an overview of the current situation of networked manufacturing and knowledge flow is given and some technologies that are about the main studying field of this dissertation are analyzed, such as resource space model, knowledge management, web collaboration, controlling technique etc. On the basis of current study of knowledge flow, the research background, main contents, purpose and importance of this dissertation are also discussed.In chapter 2, resource space model is used in knowledge modeling, and its properties are extended. The matrix description, combination, integration, semantic reasoning and semantic search on knowledge by resource space model are studied. All of these are the theory and method basis of control on knowledge flow.In chapter 3, in terms of the extended properties of resource space model and the combination, offshoot, feedback of knowledge flow combined with the process modeling elements, the structure chart of knowledge flow process model can be ascertained. On the basis of the state structure chart, the mathematical model on process control of knowledge flow can be built through chosing state variables. A bus model of knowledge flow control is presented, and based on the control model the knowledge flow engine is established. A building method of mathematical model on process control of knowledge flow is presented for several common knowledge flow types, such as series, parallel, feedback etc.In chapter 4, on the basis of knowledge flow control model, the control strategy of knowledge flow is presented. The implementating structure of dynamic control is studied and the resource space model is applied to the state description of knowledge node to acquire the state information in networked manufacturing. The multi-aim decision on dynamic control of knowledge flow is presented and the knowledge active pushing strategy driven by workflow is studied.In chapter 5, after several existing workflow modeling methods are analyzed, the ontology model of workflow for integration with knowledge flow is proposed. While the series, parallel, choice and feedback type of workflow are realized through workflow ontology and the relationship between knowledge flow and workflow is studied in networked manufacturing, in which the integrating control model and integrating control frame between knowledge flow and workflow are discussed. At the end of this chapter several integrating control algorithms for knowledge flow and workflow are given, which are based on the limit of knowledge provider, the limit of the time and the changement of knowledge acquisition.In chapter 6, supported by China Natural Science Fund, China 863 Hi-tech Program and Zhejiang Province Manufacturing Enterprise Information Program, a system framework on the control of knowledge flow in networked manufacturing is presented and a knowledge flow management tool, a knowledge base management tool, a resource space model management tool and a workflow management tool are developed to realize the control of knowledge flow in networked manufacturing. An example on eddy current retarder is given to test the validity and the practicability of the control of knowledge flow in networked manufacturing.In chapter 7, all achievements of this dissertation are summarized and the future research work is put forward.