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

Published

Author(s)

Ruimin Chen, Yan Lu, Paul Witherell, Timothy Simpson, Soundar Kumara, Hui Yang

Abstract

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.
Citation
IEEE Journal Of Robotics And Automation

Keywords

Bayesian Network, Ontology, Additive Manufacturing, Causal Network, Causal Inference

Citation

Chen, R. , Lu, Y. , Witherell, P. , Simpson, T. , Kumara, S. and Yang, H. (2021), Ontology-driven Learning of Bayesian Network for Causal Inference and Quality Assurance in Additive Manufacturing, IEEE Journal Of Robotics And Automation, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932380 (Accessed April 15, 2024)
Created June 16, 2021, Updated November 29, 2022