Lu, Y.
, Yang, Z.
, Li, S.
, Zhao, Y.
, Xie, J.
, Safdar, M.
and Ko, H.
(2024),
An Overarching Quality Evaluation Framework for Additive Manufacturing Digital Twin, 2024 IEEE 20th International Conference on Automation Science and Engineering, Bari, MD, US
(Accessed June 30, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
An Overarching Quality Evaluation Framework for Additive Manufacturing Digital Twin
Published
Author(s)
, , , Yaoyao Fiona Zhao, Jiarui Xie, Mutahar Safdar, Hyunwoong Ko
Abstract
The key differentiation of digital twins from existing models-based engineering approaches lies in the continuous synchronization between physical and virtual twins through data exchange. The success of digital twins, whether operated automatically or with humans in the middle, hinges on the quality of data, models, and their computations, which influences their usability and effectiveness. This paper provides a framework for tracking digital twin performance in Additive Manufacturing applications and for developing quality assessment tools to enhance the development and applications of AM digital twins. This framework is founded on a digital twin development activity model, which identifies the digital objects through the digital twin evolution life cycle, delineating their quality measures and the transfer of any quality-related problems. Quality metrics are defined for each type of digital objects in the framework and computation methods are outlined to track how quality issues in earlier stages affect subsequent activities and digital objects. The data characteristics linked to digital twin effectiveness, as identified by the framework, could serve as key performance indicators for AM data management. Furthermore, understanding the challenges in the uncertainty and quality transition between digital objects can lead to a strategic research agenda for AM digital twins.Proceedings Title
2024 IEEE 20th International Conference on Automation Science and Engineering
Conference Dates
August 28-September 1, 2024
Conference Location
Bari, MD, US
Conference Title
IEEE 20th International Conference on Automation Science and Engineering
Pub Type
Conferences
Keywords
Digital twin, additive manufacturing, quality management, uncertainty quantification
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created April 15, 2024, Updated June 24, 2024