Modularized construction with precast concrete elements has many advantages, such as shorter construction times, higher quality, flexibility, and lower costs. These advantages are mainly due to its potential for prefabrication and series production. However, the production processes are still craftsmanship, and automation rarely occurs. Fundamental to the automation of production is digitization. In recent years, the manufacturing industry made significant progress through the intelligent networking of components, machines, and processes in the introduction of Industry 4.0. A key concept of Industry 4.0 is the digital twin, which represents both components and machines, thus creating a dynamic network in which the participants can communicate with each other. So far, BIM and digital twins in construction have focused mainly on the structure as a whole and do not consider feedback loops from production at the component level. This paper proposes a framework for a digital twin for the industrialized production of precast concrete elements in series production based on the asset administration shell (AAS) from the context of Industry 4.0. For this purpose, relevant production processes are identified, and their information requirements are derived. Data models and corresponding AAS for precast concrete parts will be created for the identified processes. The functionalities of the presented digital twin are demonstrated using the use case of quality control for a precast concrete wall element. The result shows how data can be exchanged with the digital twin and used for decision-making.

Model-based systems engineering (MBSE) is an auspicious approach to the virtual development of cyber-physical systems. The behavior of the system’s elements is thus represented by specialized simulation models that are integrated into the descriptive SysML-based system model. Although many simulation models have been developed in research for the common system elements for various purposes and fidelities, their integration remains a major challenge: the parameter interfaces of the simulation models must be coupled with each other and with the parameters of the system elements in such a way that they are correctly parameterized. So far, this coupling can only be carried out by model experts in a time-consuming and error-prone manner. Therefore, in this paper, we first propose a concept that structures the system element parameters for targeted use in validation and design cases. Second, we propose a model signature for simulation models that differentiates its parameters by input, internal, output, and model parameters and specifies them with spatial and temporal dimensions as well as admissible ranges, among others. Based on the two contributions, domain models can be validly and automatable coupled and used for the virtual development of system elements in model-based systems engineering.

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The re-use of product knowledge is vital to the development of Knowledge-Based Engineering (KBE) systems and to the deployment of Product Lifecycle Management (PLM) strategies. This paper addresses the challenges related to KBE-PLM systems integration in order to unlock engineering knowledge from proprietary representations and to manage the lifecycle of KBE models as well as their usage by different design automation applications. Essential constituents of product knowledge are identified and analyzed and the concepts of design intent and design rationale are reintroduced as key enablers to re-use this product knowledge in the appropriate KBE context. The paper introduces a KBE-PLM integration framework including a platform-independent Open KBE repository structured according to the KBE-PLM integration schema. This schema is a multi-layer neutral product and knowledge data model designed for integrating information from parameterized Computer-Aided Design (CAD) models, rule-based KBE systems and PLM systems.

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