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Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

Author

Listed:
  • Jakob Trauer

    (Laboratory of Product Development and Lightweight Design, Department of Mechanical Engineering, TUM School of Engineering and Design, Technical University of Munich, 85748 Munich, Germany
    Shared co-first authorship. Both authors have contributed equally.)

  • Simon Pfingstl

    (Laboratory of Product Development and Lightweight Design, Department of Mechanical Engineering, TUM School of Engineering and Design, Technical University of Munich, 85748 Munich, Germany
    Shared co-first authorship. Both authors have contributed equally.)

  • Markus Finsterer

    (Hammerer Aluminum Industries Extrusion GmbH, 5282 Ranshofen, Austria)

  • Markus Zimmermann

    (Laboratory of Product Development and Lightweight Design, Department of Mechanical Engineering, TUM School of Engineering and Design, Technical University of Munich, 85748 Munich, Germany)

Abstract

Industry 4.0, cyber-physical systems, and digital twins are generating ever more data. This opens new opportunities for companies, as they can monitor development and production processes, improve their products, and offer additional services. However, companies are often overwhelmed by Big Data, as they cannot handle its volume, velocity, and variety. Additionally, they mostly do not follow a strategy in the collection and usage of data, which leads to unexploited business potentials. This paper presents the implementation of a Digital Twin module in an industrial case study, applying a concept for guiding companies on their way from data to value. A standardized use case template and a procedure model support the companies in (1) formulating a value proposition, (2) analyzing the current process, and (3) conceptualizing a target process. The presented use case entails an anomaly detection algorithm based on Gaussian processes to detect defective products in real-time for the extrusion process of aluminum profiles. The module was initially tested in a relevant environment; however, full implementation is still missing. Therefore, technology readiness level 6 (TRL6) was reached. Furthermore, the effect of the target process on production efficiency is evaluated, leading to significant cost reduction, energy savings, and quality improvements.

Suggested Citation

  • Jakob Trauer & Simon Pfingstl & Markus Finsterer & Markus Zimmermann, 2021. "Improving Production Efficiency with a Digital Twin Based on Anomaly Detection," Sustainability, MDPI, vol. 13(18), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10155-:d:633026
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    References listed on IDEAS

    as
    1. Markus Goldstein & Seiichi Uchida, 2016. "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
    2. Poorya Ghafoorpoor Yazdi & Aydin Azizi & Majid Hashemipour, 2018. "An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach," Sustainability, MDPI, vol. 10(9), pages 1-28, August.
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    Cited by:

    1. Rafał Trzaska & Adam Sulich & Michał Organa & Jerzy Niemczyk & Bartosz Jasiński, 2021. "Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions," Energies, MDPI, vol. 14(23), pages 1-21, November.

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