A Connective Framework to Support the Lifecycle of Cyber–Physical Production Systems

Abstract

The potential benefits of the adoption of cyber-physical production systems (CPPSs) and their significant role in enabling smart manufacturing is well recognized today. However, it is less clear how such CPPS can be most effectively and consistently engineered and maintained throughout their lifecycle due to the existing divide in the information technology (IT) and operational technology (OT) landscape and ad hoc integration practices that result in inconsistent data and data models at various levels of manufacturing processes. The work presented in this article addresses this problem by envisioning a connective framework to support the engineering of CPPS through the use of a set of digital twins consistent with the real system throughout its lifecycle, not just used in the design and deployment phases. A review of the latest perspectives on using digital integration frameworks, methods, and solutions for lifecycle engineering of CPPS is provided in this article. This article demonstrates how a suitable framework, named SIMPLE, can be realized to effectively address the lack of consistent data models throughout the engineering lifecycle, including implementation details and example cases developed by the authors at the Warwick Manufacturing Group (WMG) in selected industrial Manuscript

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

“…CPPSs can be thoroughly and steadily engineered during their lifecycle in smart manufacturing through Internet of Things sensing networks, cognitive automation, real-time process monitoring, and artificial intelligence-based decision-making algorithms [88][89][90][91][92][93], even though inconstant integration routines may lead to variable data patterns at heterogeneous levels of production processes. The shift from planning to the operational phase impacts the harnessing of physical tools on the industrial unit and brings about a relevant realignment of logistical requirements throughout the plants.…”

Knowledge Integration in Smart Factories

“…"CPS are multidimensional and complex systems that integrate the cyber world and the dynamic physical world", while DTs focus more on the virtual models, the matching of their behavior to the physical counterpart and feedback flow of data between both [9]. Harrison, Vera and Ahmad (2021) envision that DTs may support the whole lifecycle and engineering of CPPS [43]. Jacoby and Usländer compared the concepts of DT and IoT and related standards or representation models [44].…”

“…Martinez et al (2021) recently conducted research where they reflected that DTs can be implemented on all levels of the classical automation pyramid while applying AI concepts, thus also indicating that a DT implementation is not only focused on one level but is a holistic approach [47]. Harrison, Vera and Ahmad (2021) also see an evolution of the automation pyramid due to "data-driven manufacturing capabilities" [43].…”

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

“…Therefore, it can be said that the total use of XR is better reflected by Figure 7A,B describing the applied technologies in the paper. CPS technologies bridge the gap between the communication of cyber and physical world while studying HRC is significant for human-centric future of smart factories [41,44]. Next, the key features and obstacles identified are introduced, after which, the studies are elaborated on further.…”

“…Moreover, CPS is often coupled with cloud computing to allow realtime monitoring of the manufacturing process [20]. With CPS, the main issues identified are integration of IT/OT due to lack of experts [44,65].…”