Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

• Main Authors: Jakob Trauer, Simon Pfingstl, Markus Finsterer, Markus Zimmermann
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Sustainability
• Subjects: Digital Twin; anomaly detection; Industry 4.0; Gaussian processes; direct bar extrusion; aluminum extrusion
• Online Access: https://www.mdpi.com/2071-1050/13/18/10155

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.