@inproceedings{e4b62fc4fc36408697047860cbf7772f,
title = "Segmented time series visualization tool for additive manufacturing",
abstract = "Additive manufacturing promises to deliver the ability to build complex shapes and parts while using raw materials more efficiently than traditional manufacturing approaches. However, material scientists are continually striving to understand how complex build parameters affect the 3D printing process and the quality of the final product. Understanding the intricate relationships between parameters and final product will yield the opportunity for automatic tuning of variables to ensure consistency of quality across build iterations.",
keywords = "additive manufacturing, dynamic time warping, segmented time series, time series mining",
author = "William Halsey and Chad Steed and Ryan Dehoff and Vincent Paquit and Sean Yoder",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016 ; Conference date: 23-10-2016",
year = "2017",
month = mar,
day = "8",
doi = "10.1109/LDAV.2016.7874336",
language = "English",
series = "IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "97--98",
editor = "Kenneth Moreland and Markus Hadwiger and Ross Maciejewski",
booktitle = "IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings",
}
