A Preliminary Methodological Approach to Models for Manufacturing (MfM)
To enhance multidisciplinary design and simulation of complex systems, MBSE (Model Based Systems Engineering) is a methodology where computer aided graphical modeling authoring tools are used to specify functions and behaviors of the systems. Simulation tools bring about a system behavioral abstraction required for the design of complex products. MBSE enables more robust systems engineering, because it results in models and their associated behavioral abstraction .
A research approach for modelling manufacturing systems in the aerospace industry, and in particular for FAL (Final Assembly Line), has been proposed by the authors in several research papers during the last years [2, 3]. Functional and data models have been published and deployed using data structures available from commercial PLM systems .
Recently a new approach for modelling manufacturing systems has been coined as an extension of the previous research to introduce MBSE in manufacturing. A new architecture based on 3-Layers Model (3LM) has been defined: a Data layer, an Ontology layer and a Service layer. Ontology layer is the core of the 3LM. The Ontology layer defines: Scope model, Data model, Behavior model and Semantic model, to further instance information from databases. Scope model is mandatory because manufacturing is a large and wide part of the artifact lifecycle and Data model can cover different several uses across it.
This paper presents a preliminary methodology for Models for Manufacturing (MfM) trends and issues that can be addressed in order to support the generation and management of manufacturing ontologies.
KeywordsModels for Manufacturing (MfM) industrial DMU (iDMU) Ontologies 3-Layers Model (3LM) Model-Based Systems Engineering (MBSE)
The authors would like to thank Ignacio Eguia, Jose Carlos Molina, Andres Padillo, and other Sevilla University colleagues, Julio Ortegon, and other Cadiz University colleagues, Rebeca Arista, Tamara Borreguero, and other AIRBUS colleagues for their support and contribution during the development of this work.
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