The demand for individualized products drives modern manufacturing systems towards greater adaptability and flexibility. This increases the focus on data-driven digital twins enabling swift adaptations. Within the framework of cyber-physical systems, the digital twin is a digital model that is fully connected to the physical and digital assets. A digital model must follow a standardization for interoperable data exchange. Established ontologies and meta-models offer a basis in the definition of a schema, which is the first phase of creating a digital twin. The next phase is the standardized and structured modeling with static use-case specific data. The final phase is the deployment of digital twins into operation with a full connection of the digital model with the remaining cyber-physical system. In this deployment phase communication standards and protocols provide a standardized data exchange. A survey on the state-of-the-art of these three digital twin phases reveals the lack of a consistent workflow from ontology-driven definition to standardized modeling. Therefore, one goal of this paper is the design of an end-to-end digital twin pipeline to lower the threshold of creating and deploying digital twins. As the task of establishing a communication connection is highly repetitive, an automation concept by providing structured protocol data is the second goal. The planning and control of a line-less assembly system with manual stations and a mobile robot as resources and an industrial dog as the product serve as exemplary digital twin applications. Along this use-case the digital twin pipeline is transparently explained.

Kurzfassung Zunehmend kurzfristige Produktintegrationen oder die Skalierung von Absatzmengen stellen kontinuierlich auftretende Herausforderungen für die industrielle Montage dar. Die resultierenden Rekonfigurationen bedingen, durch die komplexe starre Verkettung der Montagesysteme, hohe Zeitaufwände und Kosten. Als Lösungsansatz wird die Organisationsform der freien Verkettung vorgestellt, um anschließend die notwendigen Randbedingungen und erste Ansätze für eine Umsetzung aufzuzeigen.

Mass customization demands shorter manufacturing system response times due to frequent product changes. This increase in system dynamics imposes additional flexibility requirements especially on assembly processes, as complexity accumulates in this last step of value creation. Flexible and dynamically interconnected assembly systems can meet the increased requirements as opposed to traditional dedicated assembly line approaches. The high complexity and dynamical environment in these kinds of systems lead to the demand for real-time online control and scheduling solutions. Within the decision-making of online scheduling, the capability of predicting the consequences of available actions is crucial. In real-time environments, running extensive discrete-event simulations to evaluate how actions unfold requires too much computing time. Artificial neural networks (ANN) are a viable alternative to quickly evaluate the potential future performance value of a production state for online production planning and control. They can predict performance indicators such as the expected makespan given the current production status. Leveraging recent advances in artificial intelligence (AI) game algorithms, an assembly control system based on Google DeepMind’s AlphaZero was created. Specifically, an ANN is incorporated into the approach that suggests favorable job routing decisions and predicts the value of actions. The results show that the trained network can predict favorable actions with an accuracy of over 95% and estimate the makespan with an error smaller than 3%.

In manufacturing, rising demands for customized products have led to increased product variance and shortened product life cycles. In assembly lines, an increased variant diversity impedes the product flow. As a result, the utilization of assembly resources decreases, and production costs grow. An approach to increase the flexibility and adaptability of the assembly system is the implementation of the concept of line-less assembly. In the first step, the assembly line is dissolved. Then, stations are reallocated and linked by automated guided vehicles resulting in a loosely coupled layout, for example, a parallelization and interconnection of multiple lines or a matrix layout. A key requirement for the successful operation and control of a line-less assembly system is the collection and correct interpretation of data. To fully exploit the flexibility and adaptability of the concept of line-less assembly, a software architecture for planning and control must base on an information model allowing the fast integration of all shop floor assets and other data resources. Therefore, a modular data model with standardized interfaces for interoperable data exchange like a digital twin is needed. The aim of this paper is the development and implementation of a software architecture for planning and control of a line-less assembly system. Moreover, the architecture should integrate an interoperable digital twin of the physical system. To satisfy the criteria of interoperability and fast deployment, the digital twins are evolved following the methodology of a digital twin pipeline. Furthermore, a physical demonstrator serves as a testbed for the developed software architecture and digital twins. On the level of production planning and control, relevant industrial applications are identified and implemented in the form of use cases to show the functionality of the line-less assembly system as cyber-physical production system.

Increasing product variety, shorter product life cycles, and the ongoing transition towards electro-mobility demand higher flexibility in automotive pro-duction. Especially in the final assembly, where most variant-dependent pro-cesses are happening, the currently predominant concept of flowing line assem-bly is already been pushed to its flexibility limits. Line-less assembly systems break up the rigid line structures by enabling higher routing and operational flex-ibility using individual product routes that are takt-time independent. Hybrid ap-proaches consider the combination of line and matrix-structured systems to in-crease flexibility while maintaining existing structures.<br>Such system changes require a high planning effort and investment costs. For a risk-minimized potential evaluation, discrete-event simulation is a promising tool. However, the challenge is to model the existing line assembly concept and line-less assembly for comparison.<br>In this work, a comprehensive scenario analysis based on real assembly sys-tem data is conducted to evaluate the potential of line-less assembly in the auto-motive industry. Within the simulation, an online scheduling algorithm for adap-tive routing and sequencing is used. Based on an automated experiment design, several system parameters are varied full-factorially and applied to different sys-tem configurations. Various scenarios considering worker capabilities, station failures, material availability, and product variants are simulated in a discrete-event simulation considering realistic assumptions. Results show that the throughput and utilization can be increased in the hybrid and line-less systems when assuming that the stations will have failures and the assumption of an un-changed order input.