Abstract
Digital transformation is regarded as a key enabler for quality management in the modern casting industry. By uncovering parameters that affect the casting part quality, a continuous process improvement could be enabled. In particular, a focus is laid on the explicability of the identified dependencies due to the acceptance of data-driven decisions by workers as well as to initiate tailored improvement measures. For this purpose, in this paper two visual analytics approaches are discussed for uncovering quality-affecting parameters with the claim for a high degree of explicability - on the one hand a method-feature-heatmap that visualizes a multiple backward feature elimination and on the other hand a graph-based visualization of class association rules. An automotive industry use case demonstrates the approaches in the context of gravity die casting of aluminium knuckles.
Original language | English |
---|---|
Title of host publication | 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) |
Publisher | IEEE |
ISBN (Electronic) | 978-1-7281-2927-3 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 17th International Conference on Industrial Informatics, INDIN 2019 - Aalto University, Espoo, Finland Duration: 22 Jul 2019 → 25 Jul 2019 Conference number: 17 https://www.indin2019.org/ |
Conference
Conference | 17th International Conference on Industrial Informatics, INDIN 2019 |
---|---|
Abbreviated title | INDIN 2019 |
Country/Territory | Finland |
City | Espoo |
Period | 22/07/19 → 25/07/19 |
Internet address |