Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

Steed, Chad A.; Halsey, William; Dehoff, Ryan; Yoder, Sean L.; Paquit, Vincent; Powers, Sarah

doi:10.1016/j.cag.2017.02.005

Title: Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

Journal Article · Thu Feb 16 00:00:00 EST 2017 · Computers and Graphics

DOI:https://doi.org/10.1016/j.cag.2017.02.005· OSTI ID:1346683

^[1]; Halsey, William ^[1]; Dehoff, Ryan ^[2]; Yoder, Sean L. ^[2]; Paquit, Vincent ^[3]; Powers, Sarah ^[4]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Sciences and Engineering Division
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science and Technology Division
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Electrical and Electronics Systems Research Division
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system that allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility (MDF)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1346683

Alternate ID(s):: OSTI ID: 1411841

Journal Information:: Computers and Graphics, Vol. 63, Issue C; ISSN 0097-8493

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 22 works

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ARIMA-GMDH: a low-order integrated approach for predicting and optimizing the additive manufacturing process parameters Aljarrah, Osama; Li, Jun; Huang, Wenzhen The International Journal of Advanced Manufacturing Technology, Vol. 106, Issue 1-2 https://doi.org/10.1007/s00170-019-04315-8	journal	November 2019
Additive Manufacturing of Nickel Superalloys: Opportunities for Innovation and Challenges Related to Qualification Babu, S. S.; Raghavan, N.; Raplee, J. Metallurgical and Materials Transactions A, Vol. 49, Issue 9 https://doi.org/10.1007/s11661-018-4702-4	journal	June 2018
Metaheuristic approaches in biopharmaceutical process development data analysis Gangadharan, Nishanthi; Turner, Richard; Field, Ray Bioprocess and Biosystems Engineering, Vol. 42, Issue 9 https://doi.org/10.1007/s00449-019-02147-0	journal	May 2019
Three-dimensional Analysis and Reconstruction of Additively Manufactured Materials in the Cloud-Based BisQue Infrastructure Polonsky, Andrew T.; Lang, Christian A.; Kvilekval, Kristian G. Integrating Materials and Manufacturing Innovation, Vol. 8, Issue 1 https://doi.org/10.1007/s40192-019-00126-7	journal	March 2019
Metaheuristic approaches in biopharmaceutical process development data analysis Gangadharan, Nishanthi; Turner, Richard; Field, Ray Apollo - University of Cambridge Repository https://doi.org/10.17863/cam.53315	text	January 2019
ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data Wang, Xumeng; Chen, Wei; Xia, Jiazhi arXiv https://doi.org/10.48550/arxiv.2007.15272	preprint	January 2020

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