BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20230124T171526Z
LOCATION:C141
DTSTART;TZID=America/Chicago:20221113T110000
DTEND;TZID=America/Chicago:20221113T113000
UID:submissions.supercomputing.org_SC22_sess421_ws_drbsd108@linklings.com
SUMMARY:Exploring Data Reduction Techniques for Additive Manufacturing Ana
 lysis
DESCRIPTION:Workshop\n\nExploring Data Reduction Techniques for Additive M
 anufacturing Analysis\n\nNichols, Hickman Fulp, DeBardeleben, Calhoun\n\nA
 dditive manufacturing is a rapidly growing area that has the potential to 
 revolutionize society. In order to better understand and improve this proc
 ess, scientists and engineers conduct detailed studies on the applicabilit
 y of various materials and the process that additively constructs the obje
 ct. One method of analyzing the additive process is to use cameras to take
  images of the object as it is built layer by layer. As the complexity of 
 the process, image resolution, and image capture frequency increases, so t
 oo does the volume of data generated, which can lead to data storage/movem
 ent issues. In this paper, we present an exploratory study of applying var
 ious lossless and lossy reduction techniques to an additive manufacturing 
 data set from Los Alamos National Laboratory. Results show that SZ gives t
 he best reduction ratio, ZFP yields the best accuracy, and Hybrid Data Sam
 pling is the fastest method.\n\nSession Format: Recorded\n\nRegistration C
 ategory: Workshop Reg Pass
END:VEVENT
END:VCALENDAR