Semantic Annotation-based Knowledge Representation for Smart Manufacturing: A Case of Experimental Results

摘要

In smart manufacturing, manufacturing enablers (machine tools, robots, CAD/CAM systems, monitoring systems, and alike) and human resources need knowledge for performing high-level cognitive tasks such as monitoring, understanding, predicting, decision-making, and adapting. The ever-growing knowledge-bases in the human-cyber-physical systems supply the required knowledge. The knowledge in the knowledge-bases must be human/machine-comprehensible, represented by a scalable ontology-based representation method. In reality, representation methods are mostly domain-specific and follow strict ontological formalism. This study addresses this issue by presenting a semantic annotation-based representation method. The annotation mechanism follows knowledge-type-aware concept mapping. A Java™-based computerized system, denoted as Semantically Annotated Data Visualization System (SAD-VS), is also developed for human/machine comprehensibility of the represented knowledge. The proposed annotation mechanism and SAD-VS are demonstrated in detail, considering a real-life manufacturing experiment. The findings of this study can increase the usages of experimental datasets more effectively while developing digital twins.