Abstrakt

Knowledge based support can play a vital role not only in the new fast emerging information and communication technology based industry, but also in traditional manufacturing. In this regard, several domain specific research endeavors have taken place in the past with limited success. Thus, there is a need to develop a flexible domain independent mechanism to capture, store, reuse, and share manufacturing knowledge. Consequently, innovative research to develop knowledge representation models of an engineering object and engineering process called Virtual engineering object (VEO) and Virtual engineering process (VEP) has been carried out and extensively reported. This paper proposes Virtual engineering factory (VEF), the final phase to create complete virtual manufacturing environment which would make use of the experience and knowledge involved in the factory at all levels. VEF is an experience based knowledge representation for a factory encompassing VEP and VEO within it. The novelty of this approach is that it uses manufacturer’s own previous experience and formal decisions to collect and expand intelligence for future production. The experience based collective computational techniques of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) are used to develop aforesaid models. In this article the concept and architecture of VEF is explained as well as the integration of all three levels of virtual manufacturing i.e. VEO, VEP and VEF is presented. Furthermore, a case-study is presented to validate the practical implementation of the proposed concept. The benefits of this approach are manifold as it creates the environment for collective intelligence of a factory and enhances effective decision making. The models and research presented here embody the important first step into developing the future computational setting as required by the emerging next generation of cyber-physical systems.

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Autorzy

• 

  Syed Imran Shafiq

• 

  Cesar Sanin

• 

  Edward Szczerbicki prof. dr hab. inż.

  • Katedra Zarządzania
  • Wydział Zarządzania i Ekonomii
  • Politechnika Gdańska

• 

  Carlos Toro