Visual Data Analysis of Production Quality Data for Aluminum Casting

Nikolina Jekic, Manuela Schreyer, Steffen Neubert, Belgin Mutlu, Tobias Schreck

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Today's manufacturing industry is shaped by the Industry 4.0 vision, which is to increase the number of individual goods produced while minimizing the production costs and time. To increase the production outcome and quality, users need to continuously monitor and adjust the entire process. While the recent advances in sensor technology can help users to collect, produce and exchange data, human beings are often overwhelmed by the amount of data being collected. Still, the human visual system is a powerful tool that can be used to decode and process large datasets. To make intelligent use of this ability, we have developed an interactive visual data analysis tool called ADAM that can support production data exploration in the aluminum industry. Furthermore, we demonstrate the effectiveness of our tool using real production data and present insights which could be gained from use of our tool by domain experts.

Originalspracheenglisch
TitelProceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
Redakteure/-innenTung X. Bui
Herausgeber (Verlag)ScholarSpace
Seiten1487-1495
Seitenumfang9
ISBN (elektronisch)9780998133140
PublikationsstatusVeröffentlicht - 2021
Veranstaltung54th Annual Hawaii International Conference on System Sciences: HICSS 2021 - Virtual, Online, USA / Vereinigte Staaten
Dauer: 4 Jan 20218 Jan 2021

Publikationsreihe

NameProceedings of the Annual Hawaii International Conference on System Sciences
Band2020-January
ISSN (Print)1530-1605

Konferenz

Konferenz54th Annual Hawaii International Conference on System Sciences
LandUSA / Vereinigte Staaten
OrtVirtual, Online
Zeitraum4/01/218/01/21

ASJC Scopus subject areas

  • !!Engineering(all)

Fields of Expertise

  • Information, Communication & Computing

Fingerprint

Untersuchen Sie die Forschungsthemen von „Visual Data Analysis of Production Quality Data for Aluminum Casting“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren