Developing a communication architecture for improving production efficiency in smart manufacturing

Abstract

Smart manufacturing units have become the latest manufacturing standard within Industry 4.0 for production floor requirements that enable functionality and data structures for the next-generation manufacturing scene. Accompanying these requirements, functionality and structures, there has been a vast sea of research to define requirements within smart manufacturing. These requirements can be satisfied with higher-order structures and lower-level implementations sharing strategies to naturally allow for the flow of requirements and data. The omission of consistency between higher-level and lower-level implementations often leads to lacklustre implementation of smart manufacturing setups, resulting in production inefficiencies and bottlenecks. These issues come as a direct contradiction of the solutions proposed for resolution in smart manufacturing. A literature review of Industry 4.0 and smart manufacturing reveals that there are similarities and complementing features for communication flows throughout all levels of a manufacturing setup. This study classifies these requirements between different levels using a communication architecture. This architecture type looks at the flow of information, organisation of data and a set of rules for responsibilities between levels. These additions allow for control and responsibility of data at each level, allowing for consistency and traceability throughout a manufacturing setup. This study outlines objectives to benchmark a current manufacturing setup in Simulink, identify its production efficiency and other metrics outlined for improvement. Additional objectives for creating a communication architecture, scoring the implementation of this new architecture, comparing the production metrics of the communication architecture against the benchmark and outlining the lifecycle of this architecture follow. Other authors approach the problem of consistency and production improvements with historically altered architecture from software and hardware domains; however, this study evaluates the use of a communication architecture to suggest the selection of this architecture in its applicable scenarios. The communication architecture implementation is tested on the benchmark Simulink model where the results discern meaningful cause-and-effect improvements with traits such as data organisation and responsibilities as opposed to coding timing improvements. The results are discussed to highlight how the communication architecture naturally allows for its requirements to leverage the possibility to include intelligence within smart manufacturing units.