Smart manufacturing based on cyber-physical systems and beyond

Abstract

Cyber-physical systems (CPS) have gained an increasing attention recently for their immense potential towards the next generation smart systems that integrate cyber technology into the physical processes. However, CPS did not initiate either smart factories or smart manufacturing, and vice versa. Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0. Also emerging are other related models such as cloud manufacturing, social manufacturing and proactive manufacturing with the introduction of cloud computing (broadly, the Internet of Services, IoS), social networking (broadly, the Internet of People, IoP) and big data (broadly, the Internet of Content and Knowledge, IoCK), respectively. At present, there is a lack of a systemic and comprehensive study on the linkages and relations between these terms. Therefore, this study first presents a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS. Then, the paper addresses CPS-based smart manufacturing as an eight tuple of CPS, IoT, IoS and IoCK as elements. Further, the paper extends the eight-tuple CPS-based manufacturing to social-CPS (SCPS) based manufacturing, termed wisdom manufacturing, which forms a nine tuple with the addition of one more element, the IoP, and which is based on the SCPS instead of CPS. Both architectures and characteristics for smart and wisdom manufacturing are addressed. As such, these terms' linkages are established and relations are clarified with a special discussion. This study thus contributes as a theoretical basis and as a comprehensive framework for emerging manufacturing integration.

Machining Phenomenon Twin Construction for Industry 4.0: A Case of Surface Roughness

“…It integrates Information and Communication Technologies (ICT) with manufacturing activities as intensively as possible. The goal is to fulfill some high-level functional requirements such as monitoring, as monitoring, understanding, predicting, decision-making, and adapting [2,[4][5][6][7]. To achieve the above-mentioned requirements, Industry 4.0 requires some knowledge-centric embedded systems such as the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Cyber-Physical Systems (CPS) [8][9][10][11][12].…”

“…It is also found that to validate whether or not a simulated surface roughness resembles the expected roughness, the outcomes of the possibility distribution-based computing and DNA-based computing are more effective than the outcomes of a conventional computing wherein the arithmetic mean height of surface roughness is calculated. Thus, apart from the conventional computing approaches, the leading edge computational intelligence-based approaches can digitize manufacturing processes more effectively.understanding, predicting, decision-making, and adapting [2,[4][5][6][7]. To achieve the above-mentioned requirements, Industry 4.0 requires some knowledge-centric embedded systems such as the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Cyber-Physical Systems (CPS) [8][9][10][11][12].…”

“…Consider CPS. These systems are nothing but ever-growing knowledge-based systems that ensure a seamless merger between the physical and cyber worlds [6,10,13,14]. The physical world refers to the manufacturing enablers (e.g., machines, tools, sensors, physical networks among computing devices, actuators, robots, computers, and the like).…”

The development of an ontology for describing the capabilities of manufacturing resources

“…machines or workpieces, which combine with the digital world, i.e. sensors and intelligence, and are thus able to communicate, act and control themselves and each other (Baheti and Gill 2011;Yao et al 2017). In Thoben et al's (2017) review of the current Industry 4.0 and Smart Manufacturing initiatives they identified the standardization of both interfaces and information models as one of the most relevant research issues for smart manufacturing.…”

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

“…For instance, in industrial systems such as smart electric grids, there is a need of decentralised, adaptive CPSs framework towards their operative automation and optimal performance [259]. Other systems may also benefit from the use of MAS and complex networks over their CPSs, from smart manufacturing and logistics [260] to food supply chain systems [261]. • Digital twins (DT): DTs are a digital replica of physical assets and engineering systems taking into account their internal functioning and external processes that may affect their performance.…”