Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing. (April 2017)

• Record Type: Journal Article
• Authors: Steed, Chad A.; Halsey, William; Dehoff, Ryan; Yoder, Sean L.; Paquit, Vincent; Powers, Sarah
• Is Part Of: Computers & graphics. Volume 63(2017)
• Page Start: 50
• Page End: 64
• Publication Date: 2017-04
• Subjects: Visual analytics -- Information visualization -- Time series data -- Additive manufacturing -- Exploratory data analysis; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2017.02.005
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: A scalable visual analytics system for finding temporal and statistical patterns at variable scales in large and complex time series data is proposed. A new segmented time series visualization technique is introduced that provides synchronized views of time series and imagery data with visual representations of information scent derived from time series similarity algorithms. The waterfall visualization technique, which gives an overview of time series data with details in miniature form, is introduced. A practical case study led by an additive manufacturing expert and involving real 3D printer log data is presented. Future improvements for this visual analytics system and reflections on this multidisciplinary project are discussed. Graphical abstract: 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. In this paper, 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. 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. Although the focus of this paper is on additive manufacturing, the techniques described are applicable to the analysis of any quantitative time series.