Manufacturing Aspect of the IBCA Structure for Active Knowledge Content Representation in Product Model

摘要

Integration of all engineering activities for lifecycle of very complex multidisciplinary products in product lifecycle management (PLM) system has lead to very complex product models in leading industries. Product model was initially defined in the ISO 10303 standard by the International Organization for Standardization (ISO). In recent advanced PLM modeling, complexity of product is handled by knowledge driven self adaptive generic model while multidisciplinary conceptual modeling applies higher abstraction methods from systems engineering (SE). Company and personal experience and expertise based active knowledge is included in product model and accumulated as intelligent property (IP) of company. Higher abstraction levels have been included by using of requirement, functional, logical, and physical (RFLP) structure of product model. In the context of physical (P) level product information, product realization (PR) model structure is defined. PR includes model information for manufacturing processes, systems, and resources. The above scenario motivated research at the Laboratory of Intelligent Engineering Systems (LIES) in high level abstraction and request driven generation of RFLP and product realization (PR) structure elements in product model. This paper introduces new research results in the initiative, behavior, context and action (IBCA) structure. Two purposes of IBCA structure are to organize active IP in product model and driving generation of RFLP and PR structure elements using always actual IP. Next issue in this paper is analysis of possibility for connection PR model entities to active knowledge driven RFLP structure element definitions. In order to achieve industrially applicable solution, research results in IBCA structure can be implemented in any host industrial PLM system where RFLP and PR structures are available within a contextual product model system. IBCA structure is required to utilize special problem solving capabilities of soft computing methods.