Knowledge graph modeling method for product manufacturing process based on human–cyber–physical fusion

Abstract

The data generated in the product manufacturing process are usually distributed in different formats, triggering fragmented knowledge and disconnected information. To address this problem, we present a knowledge graph modeling method for the product manufacturing process. First, the concepts of human–cyber–physical (HCP) elements are analyzed in detail. The HCP-related classes, attributes, and relations are defined in a formalized manner in the ontology modeling process. Second, a knowledge graph model for the product manufacturing process (KGM/PMP) is constructed by three steps, including knowledge extraction, knowledge fusion, and knowledge reasoning. When constructing the KGM/PMP model, a deep learning method called BERT-D’BiGRU-CRF is presented to automatically extract knowledge from the manufacturing data. Moreover, a set of reasoning rules are designed to infer new knowledge. Finally, a case study is carried out to validate the effectiveness of the proposed method. The validity of the BERT-D’BiGRU-CRF method on knowledge extraction is verified by comparing performance with four other methods. The applicability of the knowledge graph model is demonstrated through developing a prototype system. With this system, manufacturing knowledge can be provided for the demanders rapidly and accurately.