Asset Administration Shell as an interoperable enabler of Industry 4.0 software architectures: a case study

In recent years, the discipline of Digital Transformation in manufacturing companies turned out to be a hot topic of research debate, which allowed the design and introduction of new technologies and tools able to exploit the potential of the data produced by the shop floor assets. This increased interest in data generation and management has however highlighted a crucial issue about the lack of standardised models and structures to share these data and ensure interoperability. Among the several concepts proposed by the recent initiatives devoted to solving or mitigating this issue, Asset Administration Shell (AAS) is increasing in popularity, given its potential in providing standardised and modular information about the assets and events represented. This paper deals with a demonstration of the easiness of integration of AAS in pre-existing software architecture, allowing higher flexibility and a better understanding of the ongoing processes: a production line has been indeed entirely represented with modular AAS metamodels and it has been used to feed a Digital Model representing the line configuration. The use case proposed proves the effectiveness of the obtained solution when used for virtual commissioning operations

The optimisation and integration of AGVs with the manufacturing process

In recent years, the manufacturing environment, driven by the growth of advanced technologies and the increasing demand for customised products, has becomes increasingly competitive. In this context, manufacturing systems are now required to be more automated, flexible and reconfigurable. Thus, Autonomous Guided Vehicle (AGV), as a key enabler of dynamic shop floor logistics, are being increasingly widely deployed into the manufacturing sector for the lineside materials supplying, work-in-progress transportation, and finished products collection. A large number of companies and institutions are researching on different AGV systems to integrate AGVs-based shop floor logistics with manufacturing equipment and processes. However, these AGV systems are typically equipped with various communication protocols and utilise ad-hoc communication methods. They lack a generic framework to integrate the AGV systems into the manufacturing systems with minimal engineering effort and system reconfiguration. Current scheduling optimisation methods for multiple AGVs in shop floor logistics now support effective task allocation, shortest route planning, and conflict-free supervision, allocating the delivery tasks based on the location and availability of AGVs. However, these current methods do not give enough consideration to real-time operational information during the manufacturing process and have difficulties in analysing the real-time delivery requests from manufacturing work stations. This not only reduces the efficiency and flexibility of the shop floor logistics, ii but also significantly impacts on the overall performance of manufacturing processes. This thesis presents a generic integration approach, called Smart AGV Management System (SAMS), to support the integration of AGVs with manufacturing processes. The proposed framework enables enhanced interoperability between AGVs-based shop floor logistics and the manufacturing process through a generic data-sharing platform. Moreover, a Digital Twin (DT)-based optimisation method is developed in SAMS that can simulate and analyse the real-time manufacturing process to schedule AGVs for optimising multiple objectives, including the utilisation of work stations, delivery Justin- time (JIT) performance, charging of AGVs and overall energy consumption. This approach is experimentally deployed and evaluated from various perspectives to identify its integration and optimisation capabilities during the reconfiguration and operational phases. The results show that the proposed integration framework can enable a more effective integration with manufacturing process compared to traditional integration methods. In addition, the results demonstrate that the proposed optimisation method can schedule and reschedule AGV-based shop floor logistics when facing a range of system disruptions