The emergence of cognitive digital twin: vision, challenges and opportunities

Figures & data

Table 1. Definitions of Cognitive Digital Twin and similar concepts from related works.

Authors	Concept	Definition
Adl (2016)	Cognitive Digital Twin	‘A digital representation, augmentation and intelligent companion of its physical twin as a whole, including its subsystems and across all of its life cycles and evolution phases.’
Fariz Saracevic (2017)	Cognitive Digital Twin	‘A virtual representation of a physical object or system across its lifecycle (design, build, operate) using real time data from IoT sensors and other sources to enable learning, reasoning and automatically adjusting for improved decision making’
Boschert, Heinrich, and Rosen (2018)	Next Generation Digital Twin	‘A description of a component, product, system or process by a set of well-aligned, descriptive and executable models’ which ‘is the semantically linked collection of the relevant digital artefacts including design and engineering data, operational data and behavioural descriptions’ and ‘evolves with the real system along the whole life cycle and integrates the currently available and commonly required data and knowledge’
Matthews (2018)	Cognitive Digital Twin	‘A digital representation and augmentation of an entity across its lifecycle to optimise cognitive activities’
Fernández et al. (2019)	Associative Cognitive Digital Twin (AC-DT)	‘A contextual augmented description that explicitly includes the relevant associated relationships, connections or information channels of the considered entity with the other entities, for the considered application and purpose’
Eirinakis et al. (2020)	Enhanced Cognitive Twin (ECT)	‘Introduce advanced cognitive capabilities to the DT artefact that enable supporting decisions, with the end goal to enable DTs to react to inner or outer stimuli. The ECT can be deployed at different hierarchical levels of the production process, i.e. at sensor-, machine-, process-, employee- or even factory-level, aggregated to allow both horizontal and vertical interplay’
Lu, Zheng et al. (2020)	Cognitive Twin	‘Digital Twins with augmented semantic capabilities for identifying the dynamics of virtual model evolution, promoting the understanding of interrelationships between virtual models and enhancing the decision-making based on DT’
Abburu et al. (2020b)	Cognitive Digital Twin (COGNITWIN)	‘An extension of Hybrid Digital Twins incorporating cognitive features that enable sensing complex and unpredicted behaviour and reason about dynamic strategies for process optimisation, leading to a system that continuously evolve its own digital structure as well as its behaviour’
Ali et al. (2021)	Cognitive Digital Twin	‘an extension of existing digital twins with additional capabilities of communication, analytics, and intelligence in three layers: i) access, ii) analytics, and iii) cognition.’
Al Faruque et al. (2021)	Cognitive Digital Twin	‘Digital Twins endowed with the other elements of cognition such as perception, attention, memory, reasoning, problem-solving, etc.’

Figure 1. Comparison between digital twins and cognitive digital twins.

Comparison between Digital Twins (DT), which contains virtual entities of only one lifecycle phase, and Cognitive Digital Twins (CDT) which covers multiple lifecycle phases and domains.

Figure 2. Relation between digital twin and cognitive digital twin.

CDT is a subset of DT, meaning that all CDTs are certain kind of DT with extended characteristics such as cognitive capabilities, cross-lifecycle phases and multiple system levels.

Figure 3. Cognitive engineering journey (adapted from Fariz Saracevic 2017).

A four-step cognitive engineering journey including connect and configure, monitor and visualize, analyse and predict, cognitive capability.

Figure 4. CDT reference architecture framework (adapted from Adl 2016).

A CDT reference architecture framework based on the Cognitive Digital Twin Core (CDTC), which is composed of six layers: anchors, surrogates, bots, perspectives, self-management, and defense systems.

Figure 5. CDT reference architecture based on RAMI4.0.

The proposed CDT reference architecture based on RAMI4.0 consisting of three dimensions: full lifecycle phases, system hierarchy levels and six functional layers.

Figure 6. The full lifecycle phases and system hierarchy levels plane of CDT reference architecture.

Figure 7. The functional layers and system hierarchy levels plane of CDT reference architecture.

Figure 8. The functional layers and lifecycle phases plane of CDT reference architecture.

Figure 9. Comparison of functional architecture between CDT and RAMI4.0.

Both the proposed CDT and the RAMI4.0 consist of physical and digital entities, but the former focuses more on the functions of the twins in the virtual space.

Figure 10. Comparison of CDT functional architecture and ISO/DIS 23247-2 DT framework for manufacturing.

Compared with ISO 23247 DT framework, the proposed CDT has an extra Twin Management functional layer, Service Orchestrator and semantic component to integrate different DTs.

Figure 11. Cognitive Twin Toolbox conceptual architecture (adapted from Abburu et al. 2020b).

The toolbox conceptual architecture proposed by Abburu et al. (2020b) to support CDT applications which contains a digital twin layer, a hybrid twin layer and a cognitive twin layer on the top.

Figure 12. CDT for decision-makings in the manufacturing (Rozanec and Jinzhi 2020).

An actionable cognitive twin application framework based on knowledge graph to support demand forecasting and production planning in a manufacturing plant.

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Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.