Performance Enhancement of Advanced Manufacturing Centers Using IIoT

Abstract

Abstract With the arising of the recent industrial revolutions 4.0 and 5.0, the relatively old term of intelligent concept has been replaced by smart one. The difference lies within the potential power of data that leverages knowledge. Whereby, the industrial systems/sub-systems induce a kind of smartness behavior termed factory or machine awareness. In that context, manufacturing renders more attention to the advances in the Internet of Things (IoT) within the industrial sector to create the Industrial Internet of Things (IIoT). The IIoT exploits the manufacturing environmental hidden data to establish smart factories. In that, the manufacturing system must have the ability to define its core information, expose the corresponding value, and drive an achievable action based on such information. This glossary applies terms such as Cyber-Physical Systems (CPS), Digital Twins (DTs), Big Data, and Cloud Computing (CC) to invoke the IIoT. Coming from diverse disciplines, CPS has to be able to cooperate with machines and manufacturing available resources to convert the current achievable machine term of multi-disciplinary to trans-disciplinary. CPS gives rise to the data engineering field to feed the Big Data engine. Such a system struggles in fronts of the CPS real-time interaction and the data processing approach. The current thesis discusses a novel approach to tackle one of the IIoT main problems in manufacturing. The study focuses on CPS-machine integration to unfold the dynamic data of the manufacturing. The study considers the energy consumption profile of a machine as life-cycle data to indicate the state of a machine. That data could be then processed to form an example Big Data information source. Seeking manufacturing enhancement, the system functions such information to optimize an advanced integrated problem of manufacturing scheduling-based application.