No abstract

The growing complexity of production systems requires appropriate control architectures that allow flexible adaptation during their runtime. Although cyber-physical production systems (CPPS) provide the means to cope with complexity and flexibility, the migration with existing control systems is still a challenge. The term CPPS denotes a mechatronic system (physical world) coupled with software entities and digital information (cyber part), both enabling the smart factory concept for the Industry 4.0 (I4.0) paradigm. In this regard, design patterns could help developers to build their software with common solutions for manufacturing control derived from experiences. We provide a description and comparison of the already existing multi-agent systems (MAS) design patterns, which were collected and classified by introducing two classification criteria to support MAS developers. The applicability of these criteria is shown in the case of specific example architectures from the lower and higher control levels. The authors, together with experts from the German Agent Systems committee FA 5.15, gathered more than twenty MAS patterns, evaluated, and compared four selected patterns with the presented criteria and terminology. The main contribution is a CPPS architecture that fulfills requirements related to the era of smart factories, as well as the Reference Architectural Model I4.0 (RAMI 4.0). The conclusions indicate that agent-based patterns greatly benefit the CPPS design. In addition, it is shown that manufacturing based on MAS is a good way to address complex requests of the CPPS development.

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.