Feature selection based on visual analytics for quality prediction in aluminium die casting

S. Gellrich, T. Beganovic, A. Mattheus, C. Herrmann, S. Thiede

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)

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 languageEnglish
Title of host publication2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
PublisherIEEE
ISBN (Electronic)978-1-7281-2927-3
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event17th International Conference on Industrial Informatics, INDIN 2019 - Aalto University, Espoo, Finland
Duration: 22 Jul 201925 Jul 2019
Conference number: 17
https://www.indin2019.org/

Conference

Conference17th International Conference on Industrial Informatics, INDIN 2019
Abbreviated titleINDIN 2019
CountryFinland
CityEspoo
Period22/07/1925/07/19
Internet address

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