Digital twins
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Digital Twins: A Comprehensive Overview
Introduction to Digital Twins
Digital twins are virtual representations of physical objects or processes that mirror their real-world counterparts in real-time. This technology has gained significant traction across various industries due to its potential to enhance decision-making, system monitoring, and lifecycle management . The concept, initially defined by Grieves in 2002, has evolved to encompass a wide range of applications, from manufacturing to smart cities and healthcare .
Key Characteristics and Frameworks
Defining Characteristics
A systematic literature review has identified 13 key characteristics of digital twins, including physical and virtual entities, environments, states, and processes. These characteristics form the foundation for understanding and developing digital twins, ensuring a common framework for future research and application.
Architectural Solutions
The architecture of digital twins is crucial for their effective implementation. Most architectural solutions employ a combination of layered and service-oriented patterns, focusing on maintainability, performance efficiency, and compatibility. Despite the lack of widely accepted reference architectures, the field is dynamic, with numerous solutions being proposed and refined.
Applications in Various Domains
Smart Farming
Digital twins hold promise for revolutionizing smart farming by enabling remote management of farm operations based on real-time data. This technology allows farmers to simulate interventions and act immediately on deviations, thereby enhancing productivity and sustainability.
Industry 4.0 and Logistics
In the context of Industry 4.0, digital twins are pivotal for transforming production and logistics. They facilitate the digital mirroring of physical assets, enriched with capabilities such as decision-making and lifecycle management. A maturity model has been developed to evaluate digital twins across their lifecycle, aiding in their application-specific improvement.
Broader Applications
Digital twins are also being applied in diverse fields such as automotive, medicine, and smart cities. They enable data-driven decision-making, complex system monitoring, and product validation, making them a versatile tool across various sectors .
Challenges and Future Research
Implementation Challenges
Despite their potential, digital twins face several implementation challenges. These include issues related to data integration, real-time information exchange, and the development of standardized protocols and frameworks. Addressing these challenges is crucial for the widespread adoption of digital twins.
Research Gaps
Current research has identified several gaps that need to be addressed to fully realize the potential of digital twins. These include understanding perceived benefits, exploring use-cases across the product lifecycle, and addressing technical implementations and data ownership issues. Further research is also needed to develop and validate comprehensive frameworks and models for digital twins.
Conclusion
Digital twins represent a transformative technology with applications spanning multiple industries. While significant progress has been made in defining their characteristics and developing architectural solutions, challenges remain in their implementation and standardization. Continued research and development are essential to overcome these hurdles and fully harness the potential of digital twins for enhanced decision-making, system monitoring, and lifecycle management.
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