Abstract
Flexible visible exploration of long, high-resolution time series from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is of paramount importance to realizing the full potential of large-scale 3D printers. In this report, we propose a visual analytics approach that helps researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. 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 with multiple levels of detail. These capabilities are applicable to the analysis of any quantitative time series. To illustrate the effectiveness of Falcon at providing thorough and rapid knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer.