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Industrial Internet of Things Safety and Security
Published in S. Velliangiri, Sathish A. P. Kumar, P. Karthikeyan, Internet of Things, 2020
Cyber manufacturing system represents a translation of data from strongly connected components into perspectives and predictive operations to achieve greater performance. Cyber manufacturing system (CMS) and IIoT systems are not separate technologies instead, they are interconnected. The main challenge with CMS and IIoT is communication among interconnected devices [4]. Efficient communication is necessary for real-time delivery of information with robustness and all quality of services. The interconnected system also needs a greater level of abstraction and self-management [6–8].
Real-time profile monitoring schemes considering covariates using Gaussian process via sensor data
Published in Quality Technology & Quantitative Management, 2023
Ning Ding, Zhen He, Shuguang He, Lisha Song
With the rapid development of Internet-of-Things and cyber manufacturing techniques (Ren et al., 2017), more and more sensors are used to gain beneficial information in many industries ranging from manufacturing to service. Sensor data provide convenience and support for data collection and further data analysis in quality management. Massive data are continuously received from sensors. Sensor data at different times are often correlated. To deal with such huge and strong-correlated data, which are hard to describe with a single or multiple quality characteristics, profiles have attracted much attention (Woodall et al., 2004). Profiles characterize the functional relationship between response and explanatory variables. Control charts are prevalent in process monitoring (Montgomery, 2020). Constructing control charts to detect profile changes is referred to as profile monitoring. Multiple excellent books and articles introduce control charts and their applications (Ahsan et al., 2020; Huwang et al., 2021; Qiu, 2013, 2018; Saha et al., 2022; Woodall & Montgomery, 2014). Comprehensive reviews are provided on studies of profile monitoring (Maleki et al., 2018; Woodall, 2007).
Augmented reality and digital twin system for interaction with construction machinery
Published in Journal of Asian Architecture and Building Engineering, 2022
Syed Mobeen Hasan, Kyuhyup Lee, Daeyoon Moon, Soonwook Kwon, Song Jinwoo, Seojoon Lee
Lockheed Martin, Boeing, General Electric Aviation, Airbus, Lufthansa and Rolls Royce Aircraft Engines all employ Digital Twin, Augmented Reality or Cyber-Physical Systems in various capacities ranging from production line monitoring to maintenance/repair. Companies that have introduced AR, DT and CPS in their manufacturing facilities also include Ford, IBM, Siemens and Vuzix; with the likes of Porsche or Mitsubishi Electric using Industry 4.0 technologies for training/operational aid purposes. According to Lee et al.: “Cyber manufacturing is the concept of intertwining IoT technologies into a transformative system that uses interconnected tether-free assets to achieve resilient performance.” (Lee, Bagheri, and Jin 2016). One idea was put forth by Ding et al. in the form of smart steel bridge construction using BIM and IoT (Ding et al. 2018). Correa et al. mention CPS integration with BIM and GIS requires a transition from Product Data Models to Process Data Models, for smart monitoring/control through the phases of a construction project to operation or maintenance (Correa and Maciel 2018).
Anarchic manufacturing: implementing fully distributed control and planning in assembly
Published in Production & Manufacturing Research, 2021
Andrew Ma, Aydin Nassehi, Chris Snider
There are few fully distributed systems investigated for assembly, despite recent increasing interest and capabilities provided through IoT and CPS technologies. Wang et al. comment that agent-based distributed manufacturing assembly has emerged for adaptive and dynamic process planning (Wang et al., 2009). Additionally, Krüger et al. propose combining decentralised and embedded controllers with machine learning for automation, to control system elements, including robotics, for flexible and reconfigurable assembly lines (Krüger et al., 2017). Antzoulatos et al. propose a MAS framework, using heterarchical with mediator structure, for plug-in/-out reconfigurable assembly resources (Antzoulatos et al., 2017); these intelligent and distributed resources align to the paradigm of CPS (L. L. Monostori et al., 2016). CPS are a network of interacting cyber and physical elements; this connects and enables communication between distributed physical objects (Leitão et al., 2016); and this would facilitate the direct communication required between elements in a distributed control system. IoT technologies provide the low-level capabilities for cyber connectivity of physical objects and has been used in cyber manufacturing to realise advanced analytics for distributed objects (Lee et al., 2016).