Advanced human–machine system for intelligent manufacturing

The use of ontologies has gained more and more interest above all for the knowledge management, e.g. the exchange of professional “know-how”, as reported in various previous papers. Under the pressure of a turbulent international market situation enterprises stress the importance of innovation in manufacturing areas. For instance, due to the drastic growing automation degree of manufacturing systems an intuitive interaction form is required, which enables the shop-floor personnel an active participation to the production without specific technical background, as well as to capture and retrieve systematically knowledge contents arising from the interaction process. The following contribution takes this topic into consideration and proposes an innovative ontology- based approach called ontological filtering system (OFS) based on methods and procedures to formalize natural language contents in a systematic way. By means of a so-called ontological network (ON) generic term forms used in the human–machine interaction (HMI) via natural language could be led back to a set of pre-defined terms. Thus, the ON consists, on the one hand, of a large number of generic natural language terms and, on the other hand, of a set of so-called key terms. The generic terms are defined, classified in semantic categories and chained together per semantic relations for a specific use in a particular domain of discourse. The key terms are used to build information on machine level and, therefore, have a formal definition. Through additional syntax roles and application-specific semantic constrains a systematic access and processing of natural language instructions is accomplished computationally. The proposed concepts have been set up and tested within an experimental testbed. The obtained results show a high system performance and encourage the research team to invest further efforts, in order to validate the system operational performances towards its industrial use at shop-floor level. Received: February 2005 / Accepted: January 2006