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

Abstract

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.

Authors:
ORCiD logo [1];  [1];  [2];  [2];  [3];  [4]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Sciences and Engineering Division
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science and Technology Division
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Electrical and Electronics Systems Research Division
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility (MDF)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office (EE-5A)
OSTI Identifier:
1346683
Alternate Identifier(s):
OSTI ID: 1411841
Grant/Contract Number:
AC05-00OR22725; 7409
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Computers and Graphics
Additional Journal Information:
Journal Volume: 63; Journal Issue: C; Journal ID: ISSN 0097-8493
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; visual analytics; data science; data visualization; additive manufacturing; time series

Citation Formats

Steed, Chad A., Halsey, William, Dehoff, Ryan, Yoder, Sean L., Paquit, Vincent, and Powers, Sarah. Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing. United States: N. p., 2017. Web. doi:10.1016/j.cag.2017.02.005.
Steed, Chad A., Halsey, William, Dehoff, Ryan, Yoder, Sean L., Paquit, Vincent, & Powers, Sarah. Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing. United States. doi:10.1016/j.cag.2017.02.005.
Steed, Chad A., Halsey, William, Dehoff, Ryan, Yoder, Sean L., Paquit, Vincent, and Powers, Sarah. Thu . "Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing". United States. doi:10.1016/j.cag.2017.02.005. https://www.osti.gov/servlets/purl/1346683.
@article{osti_1346683,
title = {Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing},
author = {Steed, Chad A. and Halsey, William and Dehoff, Ryan and Yoder, Sean L. and Paquit, Vincent and Powers, Sarah},
abstractNote = {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.},
doi = {10.1016/j.cag.2017.02.005},
journal = {Computers and Graphics},
number = C,
volume = 63,
place = {United States},
year = {Thu Feb 16 00:00:00 EST 2017},
month = {Thu Feb 16 00:00:00 EST 2017}
}

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