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Big Data Analytics pp 139–167Cite as

Automatisierte Qualitätssicherung via Image Mining und Computer Vision – Literaturrecherche und Prototyp

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Part of the Edition HMD book series (EHMD)

Zusammenfassung

Systeme zur Defekterkennung und Qualitätssicherung in der Produktion verfolgen das Ziel, Ausschussraten zu minimieren und Qualitätsstandards einzuhalten. Die dadurch angestrebte Reduktion der Produktionskosten folgt dem übergeordneten Ziel, der Maximierung der Wertschöpfung. Zu diesem Zweck lassen sich bildbasierende- sowie analytische Methoden und Techniken kombinieren. Die Konzepte Computer Vision und Image Mining bilden hierbei die Grundlage, um aus Bilddaten einen Wissensgewinn im Hinblick auf die Produktqualität zu generieren. Im Rahmen dieses Beitrages wurde ein Design Artefakt in Form eines Prototyps zur Defekterkennung und Qualitätssicherung im Bereich der Additiven Fertigung mittels eines gestaltungsorientierten Forschungsansatzes entwickelt. Die Wissensbasis für diesen Ansatz wurde innerhalb einer strukturierten Literaturanalysen erarbeitet. Der Fokus hierbei liegt auf der Identifikation und Analyse von besagten Systemen in den verschiedenen Bereichen und Branchen der Produktion. Dabei ließen sich eine Reihe von Techniken und Methoden identifizieren, die sich in den Sektor der Additiven Fertigung übertragen und gewinnbringend einsetzen lassen. Es handelt sich dabei um Methoden aus den Bereichen der Bildaufnahme, der Vorverarbeitung sowie der algorithmischen Analyse. Es konnten zudem keine Barrieren für den Einsatz von Computer-Vision- und Image-Mining-Techniken identifiziert werden, die einen Einsatz auf bestimmte Bereiche der Produktionen und Produktionsszenarien begrenzen. Die Ergebnisse dieses Beitrags stellen somit grundlegende Erkenntnisse für die Entwicklung anwendungsbezogener Defekterkennungs- und Qualitätssicherungssysteme in verschiedenen Branchen und Bereichen der Produktion dar.

Schlüsselwörter

  • Additive Fertigung
  • Computer Vision
  • Defekterkennung
  • Image Mining
  • Qualitätssicherung
  • Produktion

Vollständig überarbeiteter und erweiterter Beitrag basierend auf Trinks S, Felden C (2019) Smart Factory – Konzeption und Prototyp zum Image Mining und zur Fehlererkennung in der Produkion, HMD – Praxis der Wirtschaftsinformatik Heft 329, 56:1017–1040.

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Notes

  1. 1.

    Data Mining beschreibt den Teilschritt der Datenanalyse, die dem Zweck der Wissensentdeckung in großen Datenbeständen dient. In der Praxis wird teilweise der gesamte Prozess der Wissensaufdeckung, welcher darauf zielt implizit vorhandene, gültige, neuartige und potenziell nützlicher Muster aufzudecken, als Data Mining bezeichnet (Haneke et al. 2018).

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Author information

Authors and Affiliations

  1. Institut für Wirtschaftsinformatik, TU Bergakademie Freiberg, Freiberg, Deutschland

    Sebastian Trinks

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  1. Sebastian Trinks
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Correspondence to Sebastian Trinks .

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Editors and Affiliations

  1. IT Business Integration, Genossenschaft Migros Zürich, Zürich, Switzerland

    Dr. Sara D'Onofrio

  2. Universität Fribourg, Fribourg, Switzerland

    Prof. Dr. Andreas Meier

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Trinks, S. (2021). Automatisierte Qualitätssicherung via Image Mining und Computer Vision – Literaturrecherche und Prototyp. In: D'Onofrio, S., Meier, A. (eds) Big Data Analytics. Edition HMD. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-32236-6_7

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