Manufacturing knowledge sharing in PLM: a progression towards the use of heavy weight ontologies

Abstract

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.

Extending product lifecycle management for manufacturing knowledge sharing

“…Thus, the IMKS approach allows the derivation of specialised knowledge bases as repositories for designers and planners alike, without the need to commit to a fixed master model. In that sense, the heavyweight ontology dimension of this work builds on top of the current perceived advantages of applying formal ontologies within a PLM context to aid the process of semantic interoperability and knowledge exchanges [3,6,7,16,17].…”

“…This, therefore, raises an important concern from the point of view of semantic knowledge capture and sharing. It has been shown that OWL is limited in representing complex manufacturing constraints and process semantics [3,6]. Furthermore, although some efforts have utilised OWL with rule languages [20,27], these rule languages do not benefit from full first order logic constructs.…”

“…They also provide a basis for sharing meaning in computational form [3]. For these primary reasons, ontologies possess attractive properties as far as knowledge capture and sharing are concerned in PLM.…”

“…Various contributions have demonstrated that the semantic value of the captured knowledge is dependent on the expressiveness of the ontology language used [4,5,6]. In production engineering, heavyweight (i.e., expressive) ontologies are favoured [3,6,7] as they are accompanied with logic-based reasoning mechanisms which carefully prescribe the semantics, behaviour and conditions present within information structures. Expressive ontologies thus impart the ability to configure knowledge models for improved decision making [7].…”

Efficient management of product lifecycle information through a semantic platform

“…Lutters et al (2000) worked to apply information management based on an ontological approach on design and engineering processes under special consideration of manufacturing, i.e., process planning and cost estimation. Lemaignan et al (2006) designed an upper level manufacturing ontology (MASON), and Young et al (2007) showed the benefits of applying ontologies to support knowledge sharing in PLM with a focus on manufacturing processes. Giménez et al (2008) defined a product ontology to model complex product (the PRONTO ontology).…”