Journal of Industrial Information Integration· 2026Q1· Review
Cognitive digital twins: A state-of-the-art review
- 8citations
- Q1SCImago
- 2026year
Short summary
Cognitive Digital Twins (CDTs) integrate AI-driven cognitive functions like learning and reasoning into traditional Digital Twins, enabling more autonomous and adaptable systems for Industry 4.0/5.0.
AI-generated from the title and abstract; the full text is not read.
Key points
- Cognitive Digital Twins (CDTs) enhance traditional Digital Twins with AI-driven cognitive abilities (perception, learning, reasoning).
- CDTs enable greater system autonomy and adaptability, crucial for Industry 4.0/5.0.
- The review analyzes CDT technologies, algorithms, and frameworks, mapping cognitive functions to specific applications.
- Key gaps in current CDT development and implementation are identified to guide future research.
AI-generated from the title and abstract; the full text is not read.
Abstract
Digital twin (DT), as a key enabling technology of Industry 4.0, have transformed real-time monitoring and simulation by offering digital representations of physical systems. However, the growing complexity of modern systems needs increasingly advanced capabilities that extend beyond replication. Cognitive Digital Twin (CDT) extend the capabilities of traditional DT by integrating cognitive functions such as perception, attention, memory, reasoning, problem solving and learning through artificial intelligence (AI) techniques such as machine learning (ML). Despite the lack of literature due to the field's infancy, CDT is becoming key tools for Industry 4.0/5.0, making systems more autonomous and adaptable. This research explores CDT developments, their differences from traditional DT, and how they operationalize cognitive functions. We investigate CDT technologies through quantitative and qualitative assessments, emphasizing particular algorithms, software, tools, and frameworks across various sectors. The review identifies current advancements, maps cognitive functions to CDT applications, and highlights critical gaps, establishing a foundation for future research.
The authors' abstract, as published at the source. Journal of Industrial Information Integration, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Industrial and Manufacturing Engineering
Industrial and Manufacturing EngineeringEngineering