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

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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Article number9457187
Pages (from-to)6032-6038
Number of pages7
JournalIEEE Robotics and Automation Letters
Issue number3
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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