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Article

Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

1
Laboratory of Product Development and Lightweight Design, Department of Mechanical Engineering, TUM School of Engineering and Design, Technical University of Munich, 85748 Munich, Germany
2
Hammerer Aluminum Industries Extrusion GmbH, 5282 Ranshofen, Austria
*
Author to whom correspondence should be addressed.
Shared co-first authorship. Both authors have contributed equally.
Academic Editors: Koteshwar Chirumalla, Jessica Bruch, Anna Öhrwall Rönnbäck, Alessandro Bertoni and Anna Syberfeldt
Sustainability 2021, 13(18), 10155; https://doi.org/10.3390/su131810155
Received: 30 July 2021 / Revised: 3 September 2021 / Accepted: 6 September 2021 / Published: 10 September 2021
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. View Full-Text
Keywords: Digital Twin; anomaly detection; Industry 4.0; Gaussian processes; direct bar extrusion; aluminum extrusion; quality management Digital Twin; anomaly detection; Industry 4.0; Gaussian processes; direct bar extrusion; aluminum extrusion; quality management
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MDPI and ACS Style

Trauer, J.; Pfingstl, S.; Finsterer, M.; Zimmermann, M. Improving Production Efficiency with a Digital Twin Based on Anomaly Detection. Sustainability 2021, 13, 10155. https://doi.org/10.3390/su131810155

AMA Style

Trauer J, Pfingstl S, Finsterer M, Zimmermann M. Improving Production Efficiency with a Digital Twin Based on Anomaly Detection. Sustainability. 2021; 13(18):10155. https://doi.org/10.3390/su131810155

Chicago/Turabian Style

Trauer, Jakob, Simon Pfingstl, Markus Finsterer, and Markus Zimmermann. 2021. "Improving Production Efficiency with a Digital Twin Based on Anomaly Detection" Sustainability 13, no. 18: 10155. https://doi.org/10.3390/su131810155

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