Tradition manufacturing is undergoing a significant change, mainly promoted by emerging technologies like Cyber-physical Systems (CPS) or the Industrial Internet of Things (IIoT), leading to new automated production. The rising number of dynamically interconnected elements leads to an increasing complexity in such systems, which accompanies new challenges during systems engineering and thus leads to reaching the limits of contemporary methods. While the community has recognized this, new approaches recently arose to find solutions for particular parts of this problem. However, as the need for a holistic approach emerges, the Reference Architecture Model Industry 4.0 (RAMI 4.0) has been standardized as a promising framework. While this domain-specific architecture framework is missing specifications to address all aspects of such critical infrastructure, this paper thus proposes a new modeling methodology promoting model-driven engineering (MDE) of current or future production systems based on the peculiarities of RAMI 4.0. To enable mutual architecture development across the entire system hierarchy and all stakeholders, the aspects of model-based systems engineering (MBSE) are considered. The proposed step-by-step guideline is thereby made applicable by a modeling framework called RAMI Toolbox that ensures the engineering of both greenfield and brownfield systems. Finally, the approach, including all artifacts, is evaluated based on a real-world case study proposed by Siemens.

Additive manufacturing and product configurations will become increasingly important in future production systems. This trend brings great opportunities for manufacturing companies, but also inherits challenges to overcome. Resulting from this, a heterogeneous tool-landscape has emerged, where each of the single tools is addressing a particular aspect of the value-creation network. An example for such a tool specifically targeting the engineering of such flexible production systems according to the Reference Architecture Model Industrie 4.0 (RAMI 4.0) has been proposed with the RAMI Toolbox. However, as universal frameworks are too general to deal with all aspects of developing such complex systems, other possibilities for conflictfree engineering of the system need to be available. Thus, the main contribution of this paper deals with proposing a Round-trip Engineering (RTE) approach, that allows to export previously modeled flexible production systems according to the peculiarities of RAMI 4.0 and subsequently reload external elaborated results. Thereby, a major benefit is the application of Model-based Systems Engineering (MBSE), which ensures the traceability to the remaining system components. The chosen methodology for bidirectionally exchanging the engineering data is AutomationML, which allows to store exported information from RAMI 4.0 or import such stored information into it. The RTE-approach is thereby evaluated with a real-world case study, the Siemens Fischertechnik industrial plant model.

In recent years, the manufacturing industry sector has undergone major changes. Better known by the term Industry 4.0, this trend describes the transformation of centrally managed production systems towards decentralized value creation networks. As this leads to a strong increase regarding complexity, engineering such current or future manufacturing systems becomes a difficult task. An example for such a challenging engineering process is the selection of technical implementations fulfilling all requirements and performing the needed functions. In order to support this process, model-based systems engineering (MBSE) and its expressions like model-driven architecture (MDA) proved to be promising approaches. However, semi or fully automated model transformations, which could deal with the aforementioned issue, are still yet widely unexplored in the industrial area. Therefore, this paper introduces an approach transforming the logical architecture of a system into its technical implementation by utilizing AutomationML. As the goal is to perform this step automatically, additional tool support is provided by developing specific software. The results of this approach are thereby evaluated by a real-world case study, which is applied according to the concepts of the Reference Architecture Model Industrie 4.0 (RAMI 4.0).

Nowadays, many organizations intend to convert their existing production systems towards ones that are terized by adaptability, openness, flexibility and modularity. This requires a redesign of existing information processing systems especially related to control, leading possibly to cyber-physical production systems (CPPS). However, the implementation of new control technologies will have a direct impact on the normal operational status of production while engineers will also face several challenges and obstacles in adopting intelligent automation systems. New step-wise migration strategies are required to holistically support industries in their journey towards CPPS taking into account technical, economic and social aspects. This paper discusses the migration state-of-the-art strategies, analyzing them and providing a first attempt to define a migration approach for innovative production systems.

To quickly satisfy customer needs and to respond to market changes it is eminent to develop agile manufacturing systems. These have to support integration of flexible and reconfigurable production systems as well as IT technologies across the entire value chain. While implementing advanced technologies or integrating these into new system solutions, manufacturers and system developers have to understand the current and the desired technological capabilities of the system application. Before constructing a robust technology backbone for agile manufacturing systems, it is therefore important to make a clear picture of existing standardized solutions. Though a vast variety of assessment techniques is presented in literature, it is still unclear which approach is appropriate for a technology gap analysis, especially when developing agile manufacturing system solutions. Therefore, the harmonization of assessment techniques is essential for the analysis of the technological compliance among various systems. This paper presents a harmonized approach with exemplary results in order to navigate system developers and stakeholders towards effective and robust technology solutions that are compliant with Industry 4.0 goals and independent of manufacturing application fields.

Within research and development different agent based control architectures are presented. Up to now there is no existing approach to validate the applicability of an agent architecture to control problems. This paper presents the first results of an ongoing research activity towards an interdisciplinary modeling approach to validate the quality of Multi-Agent System (MAS) architectures for the implementation of flexible control systems. It is based on the description of the methodology and its example application to a special supervisor based agent architecture.

Industrial competitiveness today means shorter product lifecycles, increased product variety and shorter time-to-market. To face this challenge, the manufacturing industry is forced by different initiatives, namely Internet of Things, Industrie 4.0 and Cyber-Physical System to move from traditional control approaches towards intelligent manufacturing control systems that are dynamically adaptable to changing production environment and flexible to different processing tasks. For years several emergent intelligent approaches such as Multi-Agent Systems, Service-oriented Architecture, Plug-and-Produce systems and Cloud technologies have been developed in a variety of research fields. This paper reviews the state-of-the-art methodologies and technologies of flexible and reconfigurable manufacturing systems outlining the differences between them and arising benefits within the vision of the PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery) project. Current trends and challenges in industrial implementation of these methodologies are reported and research opportunities are described.