Several software architectures for digital twins have been proposed, specifying their structure and key components, such as the ISO 23247 standard for manufacturing. However, systematic methodologies for developing digital twins in the manufacturing domain remain lacking. Existing research does not adequately address methodological aspects, such as composing essential digital twin components, defining their interfaces, and systematically analyzing relevant data. This gap presents a challenge for the structured development of digital twins. In this work, we introduce a systematic approach to identify key requirements for manufacturing digital twins and propose a model-driven method for their creation. Our approach enables the generation of executable digital twins based on a formalized specification. We demonstrate its applicability through multiple industrial manufacturing demonstrators, highlighting its suitability in practice. The proposed method is adaptable regarding the purpose of the digital twins to be developed and supports a largely automated process.

The concept of digital shadows helps to move from handling large amounts of heterogeneous data in production to the handling of task- and context-dependent aggregated data sets supporting a specific purpose. Current research lacks further investigations of characteristics digital shadows may have when they are applied to different levels of the automation pyramid. Within this paper, we describe the application of the digital shadow concept for two use cases in injection molding , namely geometry-dependent process configuration , and optimal production planning of jobs on an injection molding machine. In detail, we describe the creation process of digital shadows, relevant data needs for the specific purpose, as well as relevant models. Based on their usage, we describe specifics of their characteristics and discuss commonalities and differences. These aspects can be taken into account when creating digital shadows for further use cases.

Abstract Engineering digital twins is a software and systems engineering challenge for which no systematic approach exists. The Asset Administration Shell is becoming a popular foundation for digital twins in Industry 4.0 and it comes in different types that support the engineering of different kinds and parts of digital twins. We investigate how it supports realizing common requirements for digital twins. To this end, we investigate how each of the three Asset Administration Shell types can contribute to the systematic engineering of specific components of digital twins. Therefore, we analyzed popular definitions and conceptual models of digital twins and extracted requirements that at least two of them share. We compare the resulting requirements with Asset Administration Shells of different types and conclude with open challenges in the implementation of digital twins with this technology. This supports practitioners and researchers in identifying the most suitable type of Asset Administration Shell for their specific digital twin engineering needs and identifies gaps worthy of future research toward a systematic engineering of digital twins.

In modern systems engineering, domain experts increasingly utilize models to define domain-specific viewpoints in a highly interdisciplinary context. Despite considerable advances in developing model composition techniques, their integration in a largely heterogeneous language landscape still poses a challenge. Until now, composition in practice mainly focuses on developing foundational language components or applying language composition in smaller scenarios, while the application to extensive, heterogeneous languages is still missing. In this paper, we report on our experiences of composing sophisticated modeling languages using different techniques simultaneously in the context of heterogeneous application areas such as assistive systems and cyber-physical systems in the Internet of Things. We apply state-of-the-art practices, show their realization, and discuss which techniques are suitable for particular modeling scenarios. Pushing model composition to the next level by integrating complex, heterogeneous languages is essential for establishing modeling languages for highly interdisciplinary development teams.