Abstract In many recent application domains, software systems must repeatedly reconfigure themselves at runtime to satisfy changing contextual requirements. To decide which next configuration is presumably best suited is a very challenging task as it involves not only functional requirements but also non-functional properties (NFP). NFP include multiple, potentially contradicting, criteria like real-time constraints and cost measures like energy consumption. Effectiveness of context-aware reconfiguration decisions further depends on mostly uncertain future contexts which makes greedy one-step decision heuristics potentially misleading. Moreover, the computational runtime overhead for reconfiguration planning should not nullify the benefits. Nevertheless, entirely pre-planning reconfiguration decisions during design time is also not feasible due to missing knowledge about runtime contexts. In this article, we propose a model-based technique for precomputing context-aware reconfiguration decisions under partially uncertain real-time constraints and cost measures. We employ a game-theoretic approach based on stochastic priced timed game automata as reconfiguration model. This formal model allows us to automatically synthesize winning strategies for the first player (the system) which efficiently delivers presumably best-fitting reconfiguration decisions as reactions to moves of the second player (the context) at runtime. Our tool implementation copes with the high computational complexity of strategy synthesis by utilizing the statistical model checker Uppaal Stratego to approximate near-optimal solutions. We applied our tool to a real-world example consisting of a reconfigurable robot support system for the construction of aircraft fuselages. Our evaluation results show that Uppaal Stratego is indeed able to precompute effective reconfiguration strategies within a reasonable amount of time.

timeaware reconfiguration strategies based on stochastic priced timed games", written by Hendrik Göttmann, Birte Caesar,

Context-aware systems are gaining increasing importance in industry as managing and using context information opens new opportunities for reconfiguration, digital twin concepts, performance monitoring, and so forth. Today, there is an immense variety of context-aware systems, from mobile applications to complex systems which require run time reconfiguration. The diversity of methods for acquiring the relevant data, as well as the specific use case requirements, complicate the management and usage of context information. Thus, the usage of context information for the reconfiguration management of systems demands adequate processes to identify and model system-relevant context. Consequently, in this research, we present a new method for identifying and modeling the context information that is relevant to trigger a system reconfiguration as well as automated context model creation in the form of ontologies and feature models. We suggest the ontology usage to disambiguate the data from different sources. The implementation of context-aware behavior is beyond the scope of this article and will be included in future research. We validate our ideas using cases study in the wind energy domain and unmanned automated vehicle domain.Note to Practitioners—Dynamically changing environments, require systems that continuously monitor their environment and adapt when necessary to ensure safe and optimal operation. For the system to adapt, it is necessary to define which of the environmental variables are relevant and when a change should lead to a reconfiguration of the system. The method presented in this article helps practitioners to identify these environmental variables. A rapidly changing environment also means having to interact with different systems from different vendors. The relevant environmental variables could be provided by different systems or collected by different sensors that rely on different data models. To cope with this heterogeneous environment, it is useful to create a formalized and reusable context model, describing the relevant environmental variables. A formalized context model serves as a semantic layer to link heterogeneous data and enables operation in a changing environment. In this way, the result of the presented method is transformed automatically into an ontology.

The current challenges of industrial manufacturing forces producers to optimize and to digitize their facilities. The Digital Twin as a digital representation of both the product and the production is a key enabler to efficiency, flexibility, and sustainability. Unfortunately, the development of Digital Twins is sophisticated and hampered by manual tasks. This paper presents an approach to automatically create digital models of the objects which are to be represented, based on 3D CAD data. Therefore, the CAD data, which is stored as a STEP file, is analysed to extract relevant information for the following graph analysis, which is used to identify components, their dependencies and the resulting functional modules. The graph analysis results will be used in future work to implement a digital twin.

Many modern software systems continuously reconfigure themselves to (self-)adapt to ever-changing environmental contexts. Selecting presumably best-fitting next configurations is, however, very challenging, depending on functional and non-functional criteria like real-time constraints as well as inherently uncertain future contexts which makes greedy one-step decision heuristics ineffective. In addition, the computational overhead caused by reconfiguration planning at run-time should not outweigh its benefits. On the other hand, completely pre-planning reconfiguration decisions at design time is also infeasible due to the lack of knowledge about the context behavior. In this paper, we propose a game-theoretic setting for precomputing reconfiguration decisions under partially uncertain real-time behavior. We employ stochastic timed game automata as reconfiguration model to derive winning strategies which enable the first player (the system) to make fast look-ups for presumably best-fitting reconfiguration decisions satisfying the second player (the context). To cope with the high computational complexity of finding winning strategies, our tool implementation1 utilizes the statistical model-checker Uppaal Stratego to approximate near-optimal solutions. In our evaluation, we investigate efficiency/effectiveness trade-offs by considering a real-world example consisting of a reconfigurable robot support system for the construction of aircraft fuselages.

The development of digital process twins goes hand in hand with the digitalization of manufacturing's production chain, especially in the machining of parts. The essential basis of these digital process twins is the information model, which describes the properties and relationships of all relevant data and information required to realize the processing task and a digital representation. This paper describes such an information model and presents the benefits and application potentials of digital process twins in the machining of parts.

In order to approach the vision of quickly integrating new machines and their functionalities into a production plant, machines have to provide a machine-readable description of themselves and their functionalities. With such a description, it becomes possible to find required functions, check compatibility and, finally, execute production processes. In recent years, semantic web technologies have proven to be a promising enabler to realize such descriptions in the form of ontologies. But the creation of such an ontological description is an additional, tedious and error-prone task for a machine developer who might most likely not be an expert in semantic web technologies. In order to support developers in implementing machine functionalities as semantically described skills, we developed a method that highly automates all additional ontology-related tasks. The presented method makes use of information already contained in engineering artifacts, helps in adding additional information, and provides a framework to implement skill behavior. By using the proposed method, machine developers are put in a position to develop formal capability models themselves and with little additional effort.

Cyber-physical systems (CPSs) in the manufacturing domain can be deployed to support monitoring and analysis of production systems of a factory in order to improve, support, or automate processes, such as maintenance or scheduling. When a network of CPS is subject to frequent changes, the semantic interoperability between the CPSs is of special interest in order to avoid manual, tedious, and error-prone information model alignments at runtime. Ontologies are a suitable technology to enable semantic interoperability, as they allow the building of information models that lank machine-readable meaning to information, thus enabling CPSs to mutually understand the shared information. The contribution of this article is twofold. First, we present an ontology building method that is tailored toward the needs of CPSs in the manufacturing domain. For this purpose, we introduce the requirements regarding this method and discuss related research concerning ontology building. The method itself is designed to begin with ontological requirements and to yield a formal ontology. As the reuse of ontologies and other information resources (IRs) is crucial to the success of ontology building projects, we put special emphasis on how to reuse IRs in the CPS domain. Second, we present a reusable set of ontology design patterns that have been developed with the aforementioned method in an industrial use case and illustrate their application in the considered industrial environment. The contribution of this article extends the method introduced, as a postconference paper, by a detailed industrial application. Note to Practitioners-With growing digitalization in industry, the exchange and use of manufacturing-related data are becoming increasingly important to improve, support, or automate processes. Thus, it is necessary to combine information from different data sources that have been designed by different vendors and may, therefore, be heterogeneous in structure and semantics. A system that plans a maintenance worker's daily schedule, for instance, requires information about the status of machines, production plans, and inventory, which resides in other systems, such as programmable logic controllers (PLCs) or databases. When creating such information systems, accessing, searching, and understanding the different data sources is a time-intensive and error-prone procedure due to the heterogeneities of the data sources. Even worse, this procedure has to be repeated for every newly built system and for every newly introduced data source. To allow for eased access, searching, and understanding of these heterogeneous data sources, ontology can be used to integrate all heterogeneous data sources in one schema. This article contributes a method for building such ontologies in the manufacturing domain. Furthermore, a set of ontology design patterns is presented, which can be reused when building ontologies for a domain.