logo
    Distributed Knowledge-Based Product Design
    0
    Citation
    4
    Reference
    10
    Related Paper
    Abstract:
    The knowledge-based product design was analyzed in this paper firstly. Aimed the shortages that the knowledge of the existing system for product design was close and the distributed knowledge couldn't be integrated and shared, a novel distributed knowledge management system framework for product design was proposed. The main layers were studied and the key technologies of system were discussed. Ontology-based knowledge share is proposed. Knowledge ontology and knowledge ontology model are defined in terms of the design knowledge's characteristic. A novel model searching method based on ontology is proposed, where the appropriate parts model was searched through EDCOM array and similarity. Finally a distributed knowledge management system is developed on the basis of prior analysis and about an example about eddy current retarder design based on the prototype system is given.
    Keywords:
    Design knowledge
    Distributed knowledge
    In knowledge management research field, knowledge characteristic and knowledge subject are two important dimensions. This article presents a model of knowledge integration approaches based on the two dimensions. We discuss the approaches which the elementary knowledge transforms into structural knowledge in team. Base on this model, we put forward two approaches of knowledge integration in team-knowledge connection approach and personal interaction approach.
    Citations (0)
    Product design which is one of the most important stages for manufacturing is a complex activity involving a great amount of knowledge. In this paper, knowledge is divided into three types: formula knowledge, diagram knowledge, table knowledge. Based on these requirements, knowledge base is constructed. This is used to store knowledge and to support the decision-making product design. First, the knowledge representation is formally described. Then, the knowledge navigation and search strategy is detailed. A specific section is dedicated to demonstrating the knowledge application techniques according to the different knowledge types that are proposed. The proposed methods are illustrated with examples. Finally, we conclude with the presentation of application performed with the knowledge application techniques.
    Design knowledge
    Table (database)
    Decision table
    Descriptive knowledge
    Tacit Knowledge
    Knowledge modeling
    Mathematical knowledge management
    Explicit knowledge
    Citations (5)
    The robust knowledge base needs a reasonable design pattern to represent knowledge model. On the foundation of the research about relationship of knowledge model with knowledge ontology, the knowledge model defined by three categories of domain knowledge, reasoning knowledge and task knowledge is set up. Furthermore, the matching relationship mechanism of knowledge model with knowledge base is put forward. At last, according to design pattern of knowledge model, the example of knowledge base for project risk management based on ontology is implemented.
    Citations (12)
    The Digital Age has brought not only new tools but also several new methods. A Collaborative Knowledge Platform with a hybrid intelligent system may be the appropriate base of a knowledge management system to ensure inspiration and new knowledge for a professional group of individuals. The introduced concept contributes to Knowledge Collaboration and Knowledge Engineering. The method is a special form of Knowledge Engineering which involves combining machine learning algorithms with cased-based reasoning and the result is the transformation of personal knowledge to widely adaptable explicit knowledge. Individuals can learn informally while their learning route automatically generates data for reductive reasoning process, which finally leads to the opportunity of experience mining. A concept and an approach are suggested to improve the knowledge collaboration in innovative communities, and a creative problem solving process delivers the outcome in the development of a Knowledge Management System. Finally, some partial results of the design phase of an application are presented.
    Explicit knowledge
    The authors believe that current knowledge management practice significantly under-utilizes knowledge engineering technology, despite recent efforts to promote its use. They focus on two knowledge engineering processes: using knowledge acquisition processes to capture structured knowledge systematically; and using knowledge representation technology to store the knowledge, preserving important relationships that are far richer than those possible in conventional databases. To demonstrate the usefulness of these processes, we present a case study in which the drilling optimization group of a large oil and gas service company uses knowledge engineering practices to support the three facets of the knowledge management task: knowledge capture; knowledge storage; and knowledge deployment.
    Knowledge Acquisition
    Citations (99)