Ontology-Driven Learning of Bayesian Network for Causal Inference and Quality Assurance in Additive Manufacturing

Additive manufacturing (AM) enables the creation of complex geometries that are difficult to realize using conventional manufacturing techniques. Advanced sensing is increasingly being used to improve AM processes, and installing different sensors onto AM systems has yielded more data-rich environments. Transforming data into useful information and knowledge (i.e., causality detection and process-structure-property (PSP) relationship identification) is important for achieving the necessary quality assurance and quality control (QA/QC) in AM. However, causality modeling and PSP relationship establishment in AM are still in early stages of development. In this paper, we develop an ontology-based Bayesian network (BN) model to represent causal relationships between AM parameters (i.e., design parameters and process parameters) and QA/QC requirements (e.g., structure properties and mechanical properties). The proposed model enables engineering interpretations and can further advance AM process monitoring and control.

Automated summary

An ontology-based Bayesian network model is proposed in the study to represent the causal relationships between additive manufacturing parameters and quality assurance/quality control requirements, which enables engineering interpretations and advances the monitoring and control of additive manufacturing processes.