Concept and engineering development of cyber physical production systems: a systematic literature review

Wu, Xuan; Goepp, Virginie; Siadat, Ali

doi:10.1007/s00170-020-06110-2

Cited by 37 publications

(35 citation statements)

References 133 publications

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“…It is through this process of combining these technologies and the collecting of big data that a digital twin is said to be created. Wu et al (2020) directly attributes the confusion of definitions and relationships between CPS in manufacturing and Digital Twin to the lack of an agreed upon definition of CPS, which in turn is said to be due to the relative new-ness of the term. Leng et al (2020) seeks to understand and define the relationship between CPS, digital twin and IoT.…”

Section: Digital Twin and Cyber Physical Systems: Blurring Delineationmentioning

confidence: 99%

BIM, digital twin and cyber-physical systems: crossing and blurring boundaries

Douglas¹,

Kelly²,

Kassem³

2021

Computing in Construction

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Digital Twin in construction and the built environment have started to attract the attention of researchers and practitioners in recent times. Its anticipated value proposition is focussed on its capability of generating new understanding and insights into an asset at all stages of its lifecycle, exploiting diverse data sets from a multitude of sources and professions, in real or near real-time. However, there is still a significant debate about the delineation (i.e. communalities and differences) between digital twin and other related concepts, particularly Building Information Modelling (BIM) and Cyber-Physical Systems (CPS). To date, this debate has been confined to social media discussions, insights blogs and position papers. This paper addresses this challenge using a systematic review. The aim is to investigate communalities and differences between the three concepts, Digital Twin, BIM and CPS. The results of this paper are expected to foster the discussion around this theme within construction and the built environment.

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Section: Digital Twin and Cyber Physical Systems: Blurring Delineationmentioning

confidence: 99%

BIM, digital twin and cyber-physical systems: crossing and blurring boundaries

Douglas¹,

Kelly²,

Kassem³

2021

Computing in Construction

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“…CPPS can be defined as a derivative from Cyber Physical Systems (CPS), especially tailored to the production segment. Although CPS and CPPS are heavily researched in the past years, there is still no standardized definition for this technology framework (Wu et al 2020).…”

Section: Cyber Physical Production Systems (Cpps)mentioning

confidence: 99%

“…According to Wu et al (2020), the most accepted definition can be derived from the work of Cardin (2019) who extended a previous definition from Monostori et al (2016) to the following statements: i. CPPS are superordinate systems within systems.…”

Section: Cyber Physical Production Systems (Cpps)mentioning

confidence: 99%

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Transformation of a rolling mill aggregate to a Cyber Physical Production System: from sensor retrofitting to machine learning

Ralph

Sorger

Hartl

et al. 2021

Preprint

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This paper describes the transformation of a rolling mill aggregate from a stand-alone solution to a fully integrated cyber physical production system. Within this process, already existing load cells were substituted and additional inductive and magnetic displacement sensors were applied. After calibration, those were fully integrated into a six-layer digitalization architecture at the Smart Forming Lab at the Chair of Metal Forming (Montanuniversitaet Leoben). Within this framework, two front end human machine interfaces were designed, where the first one serves as a condition monitoring system during the rolling process. The second user interface visualizes the result of a resilient machine learning algorithm, which was designed using Python and is not just able to predict and adapt the resulting rolling schedule of a defined metal sheet, but also to learn from additional rolling mill schedules carried out. This algorithm was created on the basis of a black box approach, using data from more than 1900 milling steps with varying roll gap height, sheet width and friction conditions. As a result, the developed program is able to interpolate and extrapolate between these parameters as well as different initial sheet thicknesses, serving as a digital twin for data-based recommendations on schedule changes between different rolling process steps. Furthermore, via the second user interface, it is possible to visualize the influence of this parameters on the result of the milling process. As the whole layer system runs on an internal server at the university, students and other interested parties are able to access the visualization and can therefore use the environment to deepen their knowledge within the characteristics and influence of the sheet metal rolling process as well as data science and especially fundamentals of machine learning. This algorithm also serves as a basis for further integration of materials science based data for the prediction of the influence of different materials on the rolling result. To do so, the rolled specimens were also analyzed regarding the influence of the plastic strain path on their mechanical properties, including anisotropy and materials’ strength.

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“…Building upon the concept of the CPS, a CPPS is using automation technology to a greater extend [2,48]. According to [48,50,51], the following characteristics must be fulfilled: a CPPS: (i) consists of superordinate systems The hierarchy level is distinguished as follows (Figure 1): As a digitalized supply chain in the metal processing industry usually consists of multiple smart factories, often owned by different companies and in general delocalized, smart factories include several CPPSs. A CPPS serves as an extension of a CPS, referring to a system capable of acquiring, storing, analyzing data in real-time using Internet technologies, and reintegrating information from the virtual to the physical world [48], partly dismantling the classical automation pyramid [49].…”

mentioning

confidence: 99%

Big Data in the Metal Processing Value Chain: A Systematic Digitalization Approach under Special Consideration of Standardization and SMEs

et al. 2021

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Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition, analysis, and utilization of this key technology can be defined as a driver for decision-making support, process and operation optimization, and therefore increase the efficiency and effectiveness of a complete manufacturing site. Furthermore, if corresponding interfaces within the supply chain can be connected within a reasonable effort, this technology can boost the competitive advantage of all stakeholders involved. These developments face some barriers: especially SMEs have to be able to be connected to typically more evolved IT systems of their bigger counterparts. To support SMEs with the development of such a system, this paper provides an innovative approach for the digitalization of the value chain of an aluminum component, from casting to the end-of-life recycling, by especially taking into account the RAMI 4.0 model as fundament for a standardized development to ensure compatibility within the complete production value chain. Furthermore, the key role of Big Data within digitalized value chains consisting of SMEs is analytically highlighted, demonstrating the importance of associated technologies in the future of metal processing and in general, manufacturing.

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Concept and engineering development of cyber physical production systems: a systematic literature review

Cited by 37 publications

References 133 publications

BIM, digital twin and cyber-physical systems: crossing and blurring boundaries

BIM, digital twin and cyber-physical systems: crossing and blurring boundaries

Transformation of a rolling mill aggregate to a Cyber Physical Production System: from sensor retrofitting to machine learning

Big Data in the Metal Processing Value Chain: A Systematic Digitalization Approach under Special Consideration of Standardization and SMEs

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