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Data Science Approaches to Quality Control in Manufacturing: A Review of Problems, Challenges and Architecture

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1310))

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Abstract

Manufacturing environments are characterized by non-stationary processes, constantly varying conditions, complex process interdependencies, and a high number of product variants. These and other aspects pose several challenges for common machine learning algorithms to achieve reliable and accurate predictions. This overview and vision paper provides a comprehensive list of common problems and challenges for data science approaches to quality control in manufacturing. We have derived these problems and challenges by inspecting three real-world use cases in the field of product quality control and via a literature study. We furthermore associate the identified problems and challenges to individual layers and components of a functional setup, as it can be found in manufacturing environments today. Additionally, we extend and revise this functional setup and this way propose our vision of a future functional software architecture. This functional architecture represents a visionary blueprint for solutions that are able to address all challenges for data science approaches in manufacturing quality control.

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Acknowledgement

We would like to thank the German Research Foundation (DFG) for financial support of parts of this work in the Graduate School of advanced Manaufacturing Engineering (GSC 262). Parts of this work is based on earlier publications of the PREFERML project. PREFERML is funded by the German Federal Ministry of Education and Research, funding line “Forschung an Fachhochschulen mit Unternehmen (FHProfUnt)”, contract number 13FH249PX6. The responsibility for the content of this publication lies with the authors.

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Authors and Affiliations

  1. Graduate School of Excellence Advanced Manufacturing Engineering - University of Stuttgart, Stuttgart, Germany

    Yannick Wilhelm, Peter Reimann & Bernhard Mitschang

  2. Business Information Systems - Furtwangen University of Applied Science, Furtwangen, Germany

    Ulf Schreier & Holger Ziekow

  3. Institute for Parallel and Distributed Systems - University of Stuttgart, Stuttgart, Germany

    Peter Reimann & Bernhard Mitschang

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  1. Yannick Wilhelm
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  2. Ulf Schreier
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  4. Bernhard Mitschang
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  5. Holger Ziekow
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Correspondence to Yannick Wilhelm .

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Editors and Affiliations

  1. Distributed Systems Group, Technische Universität Wien, Vienna, Austria

    Schahram Dustdar

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Wilhelm, Y., Schreier, U., Reimann, P., Mitschang, B., Ziekow, H. (2020). Data Science Approaches to Quality Control in Manufacturing: A Review of Problems, Challenges and Architecture. In: Dustdar, S. (eds) Service-Oriented Computing. SummerSOC 2020. Communications in Computer and Information Science, vol 1310. Springer, Cham. https://doi.org/10.1007/978-3-030-64846-6_4

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  • DOI: https://doi.org/10.1007/978-3-030-64846-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64845-9

  • Online ISBN: 978-3-030-64846-6

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