PROCESS-ORIENTED KNOWLEDGE MANAGEMENT

Semantic knowledge representation, management, sharing, access, and re-use approaches can support Collaborative Adaptive Production Process Planning (CAPP) in a flexible and efficient as well as an effective way. Therefore, semantic-technology based representations of such CAPP knowledge integrated into a machine readable process formalization is a key enabling factor for sharing such knowledge in cloud-based semantic-enabled knowledge repositories supporting CAPP scenarios as required in the CAPP-4-SMES project [1]. Beyond that, Small and Medium Enterprises (SMEs) as represented in CAPP-4-SMES request for a standardized CAPP-oriented product-knowledgeand production-featurerepresentation that can be achieved by applying so-called Function-Block (FB) based knowledge representation models. Semantic Weband at the same time Cloud-based technologies, tool suites, and application solutions which are based on process-oriented semantic knowledge representation methodologies such as Process-oriented Knowledge-based Innovation Management (German: Wissens-basiertes Prozesess-orientiertes Innovationsmanagement, WPIM) [2] can satisfy these needs. In this way WPIM can be applied to support the semantic integration, management, access and re-use in a machine readable and integrated representation of distributed CAPP knowledge that is shared within a cloud-based centralized semantic-enabled knowledge repository. Furthermore semantic knowledge representation and querying add value to knowledge-based and computer-aided re-use of such knowledge within CAPP activities. Finally, it will pave the way towards further automating planning, simulation and optimization support in a semantic web for CAPP. Introduction, Motivation, and Problem Statement In [1] the general concept of developing a knowledgebased and process oriented CAPP support by using the WPIM method as a basis was proposed. The WPIM approach offers the possibility of modeling and representing innovation processes in a machine-readable semantic format and furthermore enables annotating the semantic process representation in a semantic way with further knowledge resources. This whole representation structure can then later be accessed by means of semantic queries. However, so far WPIM has only been applied in domains like design and development, including Product Life Cycle Management (PLM) support but has not yet been practically applied in the domain of CAPP. In parallel to the development of WPIM Wang et al. have introduced a method for representing webbased Distributed Process Planning (DPP) activities in [3], [4], and [5]. In the following we will use slightly adapted excerpts from [3] to introduce the necessary concepts and rationale of the DPP method. The DPP method includes the concepts of Meta Function Blocks (MFBs), Execution Function Blocks (EFBs) and Operation Function Blocks (OFBs). However, while Helgoson et al. state in [6] that “Today, machining-feature based approaches combined with artificial-intelligence (AI) based methods are the popular choices for process planners”, their introduced approach, which is already based on a DPP modeling-method, does not yet support machine-readability and semantic interoperability of such models as it could be achieved by utilizing semantic representations as available in nowadays semantic web technologies and as, e.g., supported by WPIM. This means, while the proposed DPP approach is very useful and valid in terms of representing the product and machining features within MFBs, EFBs and OFBs, it does not yet support semantic-web based cross-organizational and cross-domain knowledge sharing to make such knowledge more widely available, e.g., to be shared in collaborations of SMEs within CAPP activities. As this DPP knowledge is so far not available in a machine-readable semantic representation at all, the interoperability of such a representation with technologies of the semantic web and therefore with other applications and tools, like, e.g., from the area of Artificial Intelligence (AI) and Machine Learning (ML), cannot easily be achieved. International Journal of Advanced Computer Technology (IJACT)