Sensors, Vol. 24, Pages 2618: Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes

Sensors, Vol. 24, Pages 2618: Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes Sensors doi: 10.3390/s24082618 Authors: Georgia Stavropoulou Konstantinos Tsitseklis Lydia Mavraidi Kuo-I Chang Anastasios Zafeiropoulos Vasileios Karyotis Symeon Papavassiliou In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalitie...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research