Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

Jakob Trauer, Simon Pfingstl, Markus Finsterer and Markus Zimmermann
Additional contact information
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
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
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

Sustainability, 2021, vol. 13, issue 18, 1-21

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.

Keywords: Digital Twin; anomaly detection; Industry 4.0; Gaussian processes; direct bar extrusion; aluminum extrusion; quality management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/18/10155/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/18/10155/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:18:p:10155-:d:633026

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().