Flexibility, agility and resilience of value chains are becoming increasingly important. One possible approach to achieving these goals could be the paradigm of cloud manufacturing, especially the shared production scenario, where a federation of trusted partners dynamically share manufacturing capabilities, capacity, and services over digital interfaces throughout the product lifecycle. This includes an automated bidding procedure in a cross-enterprise network. The Capability, Service, Skill (CSS) model provides a common terminology for Industry 4.0 applications. The paper focuses on enabling a shared production based on the CSS model through the implementation of the Asset Administration Shell (AAS), the use of the Industry 4.0 Language (I4.0L) and Gaia-X. It shows what a centralized approach to shared production using these technologies could look like. The scenario of a negotiation process to find suitable manufacturers for a customized product is described, and the different concepts are assigned to the elaborated steps of the I4.0L bidding procedure. In addition, the required AAS and its Submodels are designed for each step of the bidding procedure. Based on the results, the necessary standardization needs are outlined.

In order to react more dynamically and flexibly to individual customer requirements, the concepts of skill-based engineering can also be applied in the field of machining. FeasibilityCheck, PreconditionCheck and SkillExecution have emerged as important basic components of complex manufacturing skills in order to be able to control skills usefully and efficiently, especially for machine tools. This paper describes the roles of each basic skill component in machining skills and shows how they can be used. Furthermore, the concept of IDs for referencing already given ParameterSets is evaluated for machining skills. A prototype implementation shows how these ParameterSetIDs can be used to efficiently interact with the basic skill components. The results show the fundamental sense of this separation of FeasibilityCheck, PreconditionCheck and SkillExecution and support the usefulness of using ParameterSetIDs.

To achieve flexible, resilient and sustainable production, the generation of dynamic cross-company supply chains gains importance. The Shared Production scenario aims at sharing manufacturing services within distributed production network through digital interfaces. A prerequisite is secure data sharing in a trustworthy network with certified participants and a common understanding concerning vocahulary, message structure and interaction patterns Gaia-X provides a solution for a secure and federated data infrastructure according to European values (ie., European Data Protection, transparency and trust). Asset Administration Shells (AAS) provide semantic and vendor-independent information models and VDI/VDE 2193 proposes message structure and interaction protocols for bidding procedures. In this paper, we present the combination of the three concepts to enable the Shared Production scenario. We elaborate on the requirements of Shared Production, develop a system architecture and model AAS Submodel templates to perform an automated bidding procedure. Additionally, the implementation is evaluated based on the bidding of production services to produce model trucks in a demonstrator ecosystem.

To improve the fault tolerance of production systems against unforeseen events, modern production systems must be able to react more autonomously with suitable solutions to reach their goals. Such events within the company, e.g., resource breakdowns, require the possibility of internal replanning of the production schedule. Multi-Agent Systems (MAS) can provide this required flexibility. To enable standardized interaction for agents, a vendor-independent description of products and production equipment, as well as their capabilities and skills, must be developed. The Asset Administration Shell (AAS) provides the standardized meta-model to achieve this goal. In this paper, a product AAS and the submodel for the required production plan are designed. Moreover, the associated MAS and interaction for the execution and replanning of the production sequence are presented. The system is validated using an assembly use-case in the real-world demonstrator environment of the SmartFactoryKL. Combining the concepts of MAS with the standardized structures of the AAS, skills and capabilities tackles the problem of MAS to be deployed in different companies. This approach enables widespread use and scalability of MAS leveraging their advantages in terms of flexibility and efficient reconfiguration.

A skill-based engineering approach uses the concept of skill to abstract machine-specific functionality with a generic interface and common behavior. A skill is treated as a "control level service" and is used to build service-oriented architectures. Though the question of robust and flexible coordination of skills is still open. Current approaches to skills' composition result in tightly-coupled control structures. To allow frequent and rapid system’s reconfiguration a framework to flexibly build complex behaviors from reusable components is needed. Behavior Trees (BTs) have proven to be a powerful tool for specifying characters' behaviors in the gaming industry and recently is getting a lot of attention in the robotics community as well as academia. An approach of using BTs for coordination of industrial skills is proposed in this paper. Combining behavior trees with the existing skill models enhances separation of concerns and modularity of control systems in the factory automation domain. Firstly, we define the requirements for a skills coordination layer and discuss some key properties of BTs that allow to satisfy these requirements. Secondly, we introduce a skill model, which utilizes BTs and has several advantages over the current skill models. Thirdly, we introduce a concept of distributed BTs to effectively distribute functionality over different computational platforms. Finally, we present a small proof of concept example to test feasibility of the proposed approach.