During the fourth industrial revolution, agile production methods have gained increasing importance to meet the growing demand for product individualization. Conventional process monitoring systems, which predominantly rely on static, statistically based approaches, are insufficient for the requirements of flexible manufacturing environments. Although numerous research initiatives have proposed machine learning (ML) based solutions for agile process monitoring, widespread adoption in industrial practice has not yet been achieved. This is partly due to a lack of system comparability and insufficient validation under realistic production conditions.

The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached.

Im Kontext von Industrie 4.0 werden die Funktionen von Produktionsmaschinen durch digitale Services immer umfangreicher. Unter anderem steht dabei die Vorhersage des Maschinenverhaltens im Fokus. Zur Abbildung des maschinendynamischen Verhaltens kommen aktuell beispielsweise die Finite-Elemente-Methode oder Mehrkörpersimulationen zum Einsatz. Jedoch gibt es bisher keine Lösung, die eine Maschine lebensbegleitend und individuell digital abbildet. Folgend wird hierfür ein neuer Ansatz vorgestellt.

Softwareentwicklung ist entscheidend für die erfolgreiche Entwicklung moderner Produktionssysteme, da sie Automatisierung, Konnektivität, Datenanalyse und Cybersicherheit ermöglicht und die digitale Transformation von Branchen vorantreibt. Die Wahl des passenden Softwareentwicklungsmodells beeinflusst den Projekterfolg maßgeblich, da diese Modelle unterschiedliche Steuerungsoptionen für Projektumfang, Kompetenzen und Anpassungsfähigkeit bieten. Um Projektmanagern die Modellauswahl zu erleichtern, wurde ein Bewertungstool entwickelt, das in dieser Arbeit näher vorgestellt wird.

The shortage of skilled workers is a major challenge facing companies in Germany today. To address this issue, intelligent machines will need to support employees in the future, with a particular focus on self-optimization. For that an approach that enables a milling machine to autonomously model its own dynamic behavior is presented in this article. In addition, a well-known company demonstrates how important the industrial implementation of such approaches is.