Research On Product Knowledge Representation For Manufacturing KM

Increasing workforce diversity, globalization of markets and competition has driven the manufacturing to demand much more powerful knowledge-based management systems, which is leading to Knowledge Management to support not only product design, configuration and reuse but also enterprise resource management and manufacturing planning & execution. The representation, exchange, integration and sharing of product knowledge is the most fundamental and crucial.During the whole lifecycle of the products, designers are no longer merely exchanging geometric data, but more general knowledge about design and the product development process, including specifications, design rules, constraints, and rationale. Furthermore, this exchange of knowledge often crosses corporate boundaries. As the complexity of products increases and product development becomes more distributed, it is a big challenge for computational frameworks to enable engineering product development, by effectively supporting the formal representation, capture, retrieval and reuse of product knowledge. And current integration efforts are usually based solely on how information is represented (the syntax) without a description of what the information means (the semantics). The goal of ontology is to create data model or scheme to efficiently store, query, modify and reason with the knowledge. Research in this field has given us a good starting point to solve this problem.In this paper, on the basis of product knowledge representation technology, especially focusing on the field of S-B-F model and ontology, an ontology-based product knowledge representation model is put forward and two related ontologies are constructed. A detailed description of the prototype OBPKMS (Ontology-Based Product Knowledge Management System) that we are designing and implementing is also designed to show that compared with traditional product data management system, the proposed method has significant advantages in enhancing description ability, intelligence of information processing, and improving system integration and knowledge sharing. As a result, conclusions are presented.