TY - JOUR
T1 - Ontology-Driven Learning of Bayesian Network for Causal Inference and Quality Assurance in Additive Manufacturing
AU - Chen, Ruimin
AU - Lu, Yan
AU - Witherell, Paul
AU - W. Simpson, Timothy
AU - Kumara, Soundar
AU - Yang, Hui
N1 - Funding Information:
The authors would like to thank the National Institute of Standards and Technology (NIST) for supporting this work. We gratefully acknowledge the CIMP-3D at Penn State University for providing the data utilized in this research, in particular Corey Dickman for designing and executing the test builds, Griffin Jones for performing the CT scans, and students Gabi Gundermann and Matt Dolack for their preliminary analysis.
Publisher Copyright:
© 2016 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
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U2 - 10.1109/LRA.2021.3090020
DO - 10.1109/LRA.2021.3090020
M3 - Article
AN - SCOPUS:85112211042
SN - 2377-3766
VL - 6
SP - 6032
EP - 6038
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 3
M1 - 9457187
ER -