Semantic data modeling for manufacturing systems digital twins

The Industry 4.0 era increases the complexity of interactions between the physical system and the cyber system in the manufacturing and production environment. Such interactions generate a large amount of information and data, which require efficient knowledge management. The development of semantic models addresses such need in the light of this complexity. The thesis work is developed through the lens of an Industry 4.0 manufacturing system in a laboratory, in order to evaluate how to effectively manage the knowledge coming from the Digital Twins of this manufacturing system. The aim for the designed model is to be robust and to be useful in practical situations. Through the work, the feasibility and the benefits of developing such kind of semantic data models in such types of manufacturing environment will be explored and evaluated. The first part of the work proposed an ontology model designed for the manufacturing systems Digital Twins and is compliant with a generally recognizable upper ontology DOLCE. The model not only provides a solid conceptual basis, but it represents a foundation for the adaptation of the model to future expansions and reconfigurations of the manufacturing system. The second part of the work evaluated the practicality of the ontology model by using it to create an automatic connected database to store and to analyze the real time data generated from the Digital Twins, attached to the field. Finally, the work presented the significance that the developed semantic data model has both from the research aspects and from the industry aspects.