No abstract

Due to the increasing integration of different disciplines, the complexity in the development of mechatronic production systems is growing. To address this issue, a multi-disciplinary design approach has been proposed, which follows the model-based systems engineering (MBSE) architecture and integrates the interdisciplinary modeling approach SysML4Mechatronics. In this article, the applicability of this approach in the machine and plant manufacturing domain is demonstrated using five use cases. These use cases are derived from industry and are demonstrated in a lab-sized production plant. The results of the application show that the approach can completely fulfil the proposed industrial requirements, namely interdisciplinary modeling, comprehensibility of system modeling, reusability of the modeling components, coupling different engineering models and checking data consistency.

Intelligent algorithms and learning are the basis for evolving smart cyber-physical production systems (CPPSs) and Industrie 4.0. A smart image detection algorithm shall be added to reduce downtime due to the extended operation of glass bottles in a yogurt producing plant. To support the engineers in doing so, a comprehensive domain-specific language (DSL), DSL4hDNCS, is introduced, enabling rapid analysis, addressing hardware/software architectures and networkrelated delays and uncertainties. DSL4hDNCS is defined by a metamodel to avoid ambiguity and enriched by aspects, such as safety, calculation power, and network transmission time. DSL4hDNCS is used to compare (re)deployment alternatives using different technologies, such as edge, fog, and cloud to implement the additional smart algorithm. The evaluation of DSL4hDNCS using an acknowledged Industrie 4.0 demonstrator plant as a case study confirmed the benefit for engineers during the redesign.

No abstract