Knowledge Reasoning for Intelligent Manufacturing Control System

• Published in: Procedia manufacturing Vol. 39; pp. 1880 - 1888
• Main Authors: Lin, Yu-Ju, Chen, Zheng-Xian, Huang, Chin-Yin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2019
• Subjects: Knowledge-Driven Approach; Ontology; SCADA; Smart Factory Control; SPARQL; Smart Factory Control; SPARQL; Ontology; Knowledge-Driven Approach; SCADA
• ISSN: 2351-9789; 2351-9789
• DOI: 10.1016/j.promfg.2020.01.250

Manufacturing Control System (MCS) is regarded to have a capability to deliver a response in the manufacturing system to cope with the unexpected events, e.g., machine failures, order changes, etc. It is the core of smart factory control. However, most of today’s MCSs are still with the Supervisory Control and Data Acquisition (SCADA) architecture, which is in conflict with the flexible and adaptive characteristics. On the other hand, knowledge-driven approach is considered an alternative of flexibility, robustness, and re-configurability. By taking the knowledge-drive initiative, this research develops an intelligent MCS (iMCS). There are two cores in iMCS: Ontology and SPARQL. The ontology classifies and describes the relationships between objects of a particular domain with tree structure, whereas SPARQL plays a role of query/inference for data or metadata. This research applies SPARQL to trigger the ontology of the manufacturing system based on the given events. With such an approach, iMCS can timely respond and control the manufacturing conditions such as a new dispatching due to machine failure. iMCS adapts the manufacturing system to the dynamic situations.