Collaborative task of entity and relation recognition for developing a knowledge graph to support knowledge reasoning for design for additive manufacturing

Additive Manufacturing (AM) is gaining acceptance as a strategic manufacturing technique and technology for allowing new product development. Due to the lack of knowledge, design for additive manufacturing (DFAM) is now a major challenge in utilizing AM's product innovation and manufacturing capabilities. The AM sector will benefit from developing an intuitive knowledge reasoning method by constructing a Knowledge Graph (KG). We presented Bidirectional Encoder Representations from the Transformers (BERT) model for collaborative entity/relation recognition to address the issue, allowing us to study and utilize the advantages of AM through knowledge reasoning for Fused Deposition Modeling (FDM) based DFAM. First, the model analyzes preprocessed text to find and extract entities. Then, the relation recognition procedure based on dependency parsing extracts the semantic relationships between the entities. To convert word segments into vectors and improve dependency parsing, we used Continuous-Bag-of-Words (CBOW) to process texts. Therefore, this allowed us to anticipate the probability output of the center word based on the n − 1 words around the input. The extracted knowledge is then visualized as a graph and stored in the Neo4j database. Following the methods above creates a KG for the FDM-based DFAM knowledge. It can be shown that BERT is a good option for handling knowledge-driven issues needing specialists by extracting the process knowledge from text data using our suggested model. We provide evidence demonstrating the model's ability to set reasonable limitations on its predictions through a KG. Additionally, we use experiments and an application case study to demonstrate the effectiveness and competitiveness of our approach.