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The drive to maximise the potential benefits of decision support systems continues to increase as industry is continually driven by the competitive needs of operating in dynamic global environments. The more extensive information support tools which are becoming available in the PLM world appear to have great potential but require a substantial overhead in their configuration. However, sharing information and knowledge in crossdisciplinary teams and across system and company boundaries is not straightforward and there is a clear need for more effective frameworks for information and knowledge sharing if new product development processes are to have effective ICT support. This paper presents a view of the current status of manufacturing information sharing using light-weight ontologies and goes on to discuss the potential for heavy-weight ontological engineering approaches such as the Process Specification Language (PSL). It explains why such languages are needed and how they provide an important step towards process knowledge sharing. Machining examples are used to illustrate how PSL provides a rigorous basis for process knowledge sharing and subsequently to illustrate the value of linking foundation and domain ontologies to provide a basis for multi-context knowledge sharing.

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Problems related to knowledge sharing in design and manufacture, for supporting automated decision-making procedures, are associated with the inability to communicate the full meaning of concepts and their intent within and across system boundaries. To remedy these issues, it is important that the explicit structuring of semantics, i.e. meaning in computation form, is first performed and that these semantics become sharable across systems. This paper proposes a Common Logic-based ontological foundation as a basis for capturing the meaning of core feature-oriented design and manufacture concepts. This foundation serves as a semantic ground over which design and manufacture knowledge models can be configured in an integrity-driven way. The implications involved in the specification of the ontological foundation are discussed alongside the types of mechanisms that allow knowledge models to be configured. A test case scenario is then analysed in order to further support and verify the researched approach.

1 In a seminal work published in 1952, "The chemical basis of morphogenesis"-considered as the true start point of the modern theoretical biology-, A. M. Turing established the core of what today we call "natural computation" in biological systems, intended as self-organizing dynamic systems. In this contribution we show that the "intentionality", i.e., the "relation-to-object" characterizing biological morphogenesis and cognitive intelligence, as far as it is formalized in the appropriate ontological interpretation of the modal calculus (formal ontology), can suggest a solution of the reference problem that formal semantics is in principle unable to offer, because of Gödel and Tarski theorems. Such a solution , that is halfway between the "descriptive" (Frege) and the "causal" (Kripke) theory of reference, can be implemented only in a particular class of self-organizing dynamic systems, i.e., the dis-sipative chaotic systems characterizing the "semantic information processing" in biological and neural systems.

Abstract:The capture of manufacturing best practice knowledge in product lifecycle management systems has significant potential to improve the quality of design decisions and minimise manufacturing problems during new product development. It should both support manufacturing engineers and offer designers an improved ability to understand the manufacturing consequences of alternative design options. However, providing a re-useable source of manufacturing best practice is difficult due to the complexity of the viewpoint relationships between products and the manufacturing processes and resources used to produce them. This paper reports on an industrial exploration of this problem combined with the application of modelling methods which support the capture of relationship knowledge during system design.The paper discusses the analysis of a number of component products and their manufacturing methods in order to identify how best to organise manufacturing best practice knowledge, the relationships between elements of this knowledge plus their relationship to product information. The representation of such complex relationships during system design typically causes problems as traditional system design tools such as UML do not readily support the capture of other than simple relationships between different information classes. This paper also explores the application of UML-2 as a system design tool which can model these relationships and hence support the reuse of system design models over time.The paper identifies a set of part family and feature libraries and, most significantly, the relationships between them, as a means of capturing best practice manufacturing knowledge and illustrates how these can be linked to manufacturing resource models and product information. The viewpoint relationships between part families and features are captured during system design using UML-2 thereby supporting the long term re-use of the knowledge as systems are developed and new systems come on line. Design for manufacture and machining best practice views are used in the paper to illustrate the concepts developed. An experimental knowledge based system has been developed and results generated using a power transmission shaft example.