Resilience in production context describes the ability to adapt efficiently to changes, to recover from problems, and to keep a production system running. The research project Werk 4.0 aims to create a resilient and flexible production environment. The foundation for this is a standardized and interconnected data model (based on an OWL ontology) that provides up-to-date and high-quality data. By introducing this ontology, configuration management, version management, and change management, inconsistent information is prevented. This enables a quick and efficient response to (unforeseen) resilience events in different resilience scenarios. The present contribution describes how resilience is approached in the context of the research project Werk 4.0.

Virtual commissioning of production plants typically relies on simple, signal-based simulation models. However, integrating process automation components such as adhesive bonding requires more detailed, physics-based modeling. This paper presents a hybrid simulation approach that augments signal-based models with continuous-time physical models to enable advanced use cases, including virtual sensors in digital shadows and optimization within digital twins. Using an adhesive bonding process from the automotive industry as an example, we demonstrate model development and integration, with a focus on defining clear model interfaces. Functional Mock-Up Units (FMUs) generated from Modelica facilitate modular integration across factory and process automation domains.

Designing and utilizing digital twins is one of the recent trends within digitalization of manufacturing industries. But this digitalization not as easy as it sounds. Different infor-mation sets coming from different life cycle phases, used within various use cases, exploiting different information provision technologies, and emerging from different engineering disciplines need to be consistently combined. Within this field technologies like asset administration shells, opeVA and AutomationML are taking a leading position but covering different life cycle phases. Within this paper a first approach is presented in-tending to overcome the integration nightmare by combining the named technologies and enabling the consistent design and application of a digital twin preserving the benefits of the integrated technologies.

For several decades production systems were considered as closed and decoupled units, where information and network security has not been an issue. This is changing rapidly, since in the age of smart factories, production systems and office IT are growing together so as to transform entire value chains into interconnected distributed systems. By this means, production systems inherit the security challenges of office IT networks connected over the Internet. Therefore and as they tend to be operated for a much longer period of time, a prospective design of security mechanisms is mandatory. For some time, the design process of production systems gets modernised by the Automation Markup Language (AutomationML, IEC 62714). AutomationML incorporates formats of all engineering phases of production systems, thus allowing engineers to model production systems on various levels of abstraction. The language also provides building blocks for modelling the network infrastructure, which are presented in the AutomationML Communication whitepaper. However, the level of detail that can be captured is currently not sufficient for modelling most network protocols and therefore any network security concept. Therefore, we propose an extension to the AutomationML Communication whitepaper and its best practice recommendations, which allows us to model networks according to the established ISO/OSI model. Using this extension we show that concepts like network separation can be modelled and validated.

Automated manufacturing systems are becoming increasingly flexible in order to support a growing number of different products and product variations, as well as shortening lot sizes and product life cycles. Many manufacturing resource are already multi-purpose. But they are integrated in an automation infrastructure that may require significant effort to adapt. In this contribution, we present a system architecture for the deployment of software functionality to manufacturing resources safely at runtime. The architecture integrates software model verification to ensure the integrity of new functionality, software-defined networking (SDN) to establish new communication pathways for collaboration, and the close interaction between a flexible runtime execution environment and its safety-critical counterpart.

A frequently denominated use case of Industrie 4.0 is plug-and-work. This use case not only requires the technical base to determine how and what information has to be exchanged during the startup of software and hardware components in the production environment, but also deals with very sensitive information which results in a high demand to secure these. In consequence, plug-and-work mechanisms which base on industrial standards such as AutomationML (IEC 62714) which defines what will be transferred and OPC UA (IEC 62541) which defines how to communicate must be embedded in a confidential environment to secure confidential information meeting the demands of the end user, e.g. plant operators. To this end, the combination of both, OPC UA and AutomationML, must face security issues. A role-based security concept based on both standards and implemented in a software tool is discussed in the present paper.

To model increasingly adaptive production systems, skills are used to describe generic capabilities of the system components. In this paper, the authors extend the well-known division of production entities into product, process, and resource (PPR) with a skill definition. There are two main advantages for this approach: First, using PPR for the skill definition allows easy integration into existing models and tools. Second, there is a natural tendency to define very generic skills to capture all possible use cases. But at some point, skills have to be translated into precise instructions for execution. The model makes this dichotomy explicit and provides a common taxonomy for stakeholders concerned with skills on different abstraction levels.

Knowledge is one of the most valuable goods within production planning. It can be divided into domain dependant knowledge and domain independent knowledge. An example for domain dependant knowledge is Manufacturing Execution System (MES) specific knowledge. An example for domain independent knowledge is the XML-based data exchange format AutomationML. Both types of knowledge are necessary. This contribution shows the application of ontology mapping and matching methods in the context of domain dependant and domain independent knowledge to support the engineer in working with the data and improve the usage of such information processing.

The plant planning process is a complex process with many different disciplines involved. It consists of multiple process steps which are executed sequentially. All the different disciplines work on the common planning object - the plant - consisting of complex mechatronic objects and parts. Nevertheless, the disciplines work spatially and technically separated from each other within their isolated planning steps. Cooperation is only practiced at specific points of the plant planning process. The Digital Engineering Table (DigET) enables the necessary interpersonal and interdisciplinary cooperation in an interactive environment and uses therefore the integrated data exchange format AutomationML. This contribution describes the requirements and decisions for the necessary middleware as coordinating infrastructure.

The wide-spread use of a standard data exchange format depends on its content, but also on its tractability and usability. In this context, one aspect is the proof of standard conformity. If the rules and definitions of a standard are violated, the created results will not conform to this standard and cannot be used by others. An easy to use verification would be the proof that the results correspond to the desired standard. This article presents a concept for formal conformance testing of AutomationML® data, an exchange format which is applied in plant engineering. The paper proposes a method for verifying conformance of an AutomationML ® instance to the AutomationML® meta model and specification.

Improving efficiency in Manufacturing Execution Systems (MES) engineering and ensuring interoperability requires a tool or a tool suite, which handles the desired (heterogeneous) data representations or better an integrated (homogeneous) data representation - a data format aiming at interoperability. CAEX (Computer Aided Engineering Exchange,) is one possible example of such a format. Its objective is a tool-independent description of plant planning data. CAEX is also used as a top-level format for AutomationML. To simplify the usage of this complex data exchange format, the authors developed various useful assistance tools which help users to work with it. These tools for transformation of arbitrary plant engineering data, their background and methodology will be presented in this contribution.

AutomationML (Automation Markup Language) is an XML based data format for the exchange of plant engineering information. Its mission is to interconnect engineering tools of different disciplines, e.g. process, mechanical or control engineering. AutomationML enables the vendor independent storage of interlinked engineering data that is usually distributed into different engineering tools. This enables a wide range of future tool functionality not available and not possible today. For this, AutomationML defines a number of extended concepts for different engineering aspects. One of them is the process-resource-product concept which was introduced with version 1.1 and published at the Hannover trade fair 2009. This paper explains this concept by means of an industrial example.

Control systems monitor and control production plants and manufacturing systems. Before they can be taken into operation, they have to be configured. To automate this process, which is called engineering, a standardized exchange format is required that forms the basis of the work of all those who are involved in the project and that is understood by anyone involved. In addition, the contents to be exchanged have to be transmitted on the basis of a standardized communication mechanism with maximum efficiency. This article will present a standardized communication method capable of fulfilling these requirements. On the basis of an OPC UA framework acting as a communication, synchronization and processing mechanism, the opportunities of the unified architecture and its application in the automated engineering of control systems will be evaluated.