The Digital Twin’s Journey of Maturity and Value
Background
The concept of the digital twin traces its origins to early space exploration, most notably NASA’s Apollo 13 mission, where a high-fidelity ground-based replica enabled engineers to simulate and validate corrective actions in real time. This foundational idea of “pairing” a physical system with its virtual counterpart laid the groundwork for the evolution of the digital twin.
The formal definition of the digital twin was introduced by Michael Grieves in 2002 within the context of product lifecycle management (PLM), comprising a physical entity, a virtual representation, and a bidirectional data link between them. Over the past two decades, advances in IoT (the Internet of Things), high-performance computing, and artificial intelligence (AI) have transformed digital twins into dynamic, predictive, and increasingly autonomous systems.
The Digital Twin Consortium defines it as “an integrated data-driven virtual representation of real-world entities and processes, with synchronized interaction at a specified frequency and fidelity” [1]. The National Academies of Sciences, Engineering, and Medicine define it as “a set of virtual information constructs that mimics the structure, context, and behavior of a natural, engineered, or social system (or system-of-systems), is dynamically updated with data from its physical twin, has a predictive capability, and informs decisions that realize value. The bidirectional interaction between the virtual and the physical is central to the digital twin” [2].
In parallel, the field of nondestructive evaluation (NDE) has undergone its own digital transformation. The emergence of NDE 4.0, as articulated by Singh and Vrana [3], redefines NDE as a fully integrated, data-centric discipline embedded within the digital engineering ecosystem. In this paradigm, inspection is no longer a discrete activity but a continuous, lifecycle-integrated function enabled by digital connectivity, automation, and intelligence.
Within NDE 4.0:
A digital twin represents the evolving condition of materials and structures, continuously updated with inspection and sensor data.
A digital thread connects inspection data across the lifecycle, from material manufacturing to in-service operation.
A digital fabric extends this concept to a system-of-systems, integrating multiple twins across assets, processes, inspectors, and organizations. Other terms for the same concept are digital weave and digital tapestry.
Thus, in the context of material evaluation, digital twins are not mere representations of assets but living models of material integrity, incorporating inspection data, degradation mechanisms, and predictive insights to support decision-making.
Up Until Now
Digital twins and digital threads have become central constructs in industrial digitalization, while the broader concept of digital fabric is emerging to describe interconnected ecosystems of data and models. However, their interpretation has remained inconsistent, often limited to data visualization or isolated inspection records.
To address this, various digital twin maturity models have been developed across industries, typically progressing from static representations to autonomous, self-optimizing systems. Some examples include:
Verdantix Model: Tailored for industrial facilities, this model focuses on increasing value through five evolutionary stages: descriptive (static visualization), informative (real-time IoT data), predictive (trend forecasting), comprehensive (what-if simulations), and autonomous (automated actions) [4].
Siemens/RWTH DTMM: This comprehensive 2D model covers the entire digital twin lifecycle across three phases: reference concept, develop, and operate. It encompasses 13 activities and 43 topics, covering both technical and nontechnical areas, including processes, tools, and security [5].
F1000Research Index: This theoretical framework maps five maturity levels (status, informative, predictive, optimization, and autonomous) to the knowledge pyramid, moving from basic data capture to full control via “wisdom” [6].
IBM Model: Emphasizing AI sophistication and data integration, IBM’s five-stage model progresses from “static” CAD/BIM renderings to “dynamic” real-time simulations and finally “autonomous” self-acting systems with closed-loop control [7].
Digital Twin Consortium (DTC) Business Model: Unlike purely technical models, the DTC framework assesses five stages (passive to master) against five key SCANNER | NDEOUTLOOK organizational elements: ambition and strategy, leadership, culture/ change/capability, operating model/ processes, and technology [8].
While these frameworks are valuable, they are largely domain-agnostic. In contrast, NDE 4.0 introduces domain-specific requirements, including:
Integration of multimodal NDE data (ultrasonic testing [UT], radiographic testing [RT], eddy current testing [ECT], thermography, etc.)
Traceability across the digital thread
Physics-based understanding of damage mechanisms
Real-time condition assessment and prognosis
Certification, validation, and regulatory compliance
This necessitates a digital twin maturity model tailored to NDE, with a focus not only on asset performance but also on material-state awareness, defect characterization, and lifecycle integrity management.
Outlook
This article presents a maturity model aligned with NDE 4.0 principles, guiding organizations from isolated inspection data toward a fully integrated digital fabric for material integrity management.
The model is structured across five levels of maturity and three core tracks:
Purpose and strategy
Cyber-physical integration
Competency
These dimensions collectively define the evolution from reactive inspection to predictive and autonomous integrity management across the lifecycle.
The Five Levels of Maturity
Level 1: The Connected Model. At this stage, NDE data is digitized and connected, enabling real-time visibility of inspection results. Data from techniques such as UT or RT is captured and stored but remains largely siloed. The digital twin is a passive representation of the current material condition without feedback capability.
Level 2: The Relational System. Inspection data becomes contextualized within the digital thread, linking material properties, manufacturing history, and operational conditions. Bidirectional data exchange enables updates between physical assets and their digital representations. Defect indications are correlated with usage and environment, improving interpretation and traceability.
Level 3: The Decisive System. Predictive capabilities are introduced through the integration of physics-based models and data- driven analytics. NDE data is used to estimate remaining useful life, predict defect growth, and assess risk. Simulation and what-if analysis enable informed decision-making regarding inspection intervals, maintenance, and material selection.
Level 4: The Autonomous System. Closed-loop systems enable real- time decision-making and automated responses. Inspection systems, analytics platforms, and operational controls are integrated such that detection of anomalies can trigger immediate actions— such as process adjustments or maintenance interventions—without human intervention. AI governance becomes critical at this stage.
Level 5: The Evolving Ecosystem. At the highest level, a digital fabric is established that integrates multiple digital twins across assets, systems, and organizations. NDE becomes a continuous, embedded function within the lifecycle, enabling collective learning across fleets and environments. Material behavior, inspection outcomes, and operational data coevolve, enabling adaptive, optimized systems.
The Three Core Tracks
The maturity model is organized across three tracks that together describe how NDE evolves in purpose, physical integration, and human capability as organizations advance through each level.
Track 1: Purpose and Strategy
This track defines the strategic evolution of NDE within a digital ecosystem:
Level 1 (Visibility): Digitize and visualize inspection data.
Level 2 (Understanding): Correlate inspection results with material behavior and context.
Level 3 (Assurance): Enable predictive integrity assessment and risk-informed decisions.
Level 4 (Orchestration): Achieve continuous integrity management with minimal human intervention.
Level 5 (Evolution): Drive lifecycle optimization and continuous improvement through system-wide learning.
Track 2: Cyber-Physical Integration
This track describes the integration of physical inspection systems with digital tools:
Level 1 (One-way/Tracked): Inspection data flows from physical systems to digital repositories.
Level 2 (Bidirectional/ Synchronized): Digital models inform inspection planning and interpretation.
Level 3 (Prescriptive): Predictive models guide inspection strategies and maintenance decisions.
Level 4 (Closed Loop/Self-Acting): Real-time inspection data triggers automated responses.
Level 5 (Adaptive System-of- Systems): Interconnected digital twins enable cross-asset learning and coordinated decision-making.
Track 3: Competency
This track outlines the capabilities required for NDE 4.0 maturity:
Level 1 (Connectivity-Ready): Digital data acquisition and basic IT/ OT (information technology/operational technology) integration.
Level 2 (Integrated): Collaboration between NDE experts, materials scientists, and data engineers.
Level 3 (Lifecycle Intelligence): Expertise in damage modeling, prognostics, and simulation.
Level 4 (System Fluency): Advanced capabilities in automation, AI, and real-time control systems.
Level 5 (Ecosystem Orchestration): Competence in multisystem governance, standards, ethics, and large- scale data integration.
Consumer Tip
As a user of digital twins and a consumer of this model, you are not tied to the exact detailed definition for each level. You may choose to tweak the tracks or even add another relevant track to better fit your context, as long as you stick to the five levels represented in the figure, which have clear delineations.
Conclusion: The Path Forward
The transition from conventional NDE practices to an NDE 4.0–enabled digital fabric represents a paradigm shift in material evaluation. Inspection is no longer a periodic, isolated activity but a continuous, intelligent process embedded within the lifecycle.
By advancing through the maturity levels outlined in this model, organizations can move from reactive defect detection to proactive and autonomous integrity management. This transformation requires not only technological investment but also the development of new competencies, standards, and governance frameworks.
Ultimately, the convergence of digital twin, digital thread, and digital fabric within the NDE 4.0 paradigm enables a future where material integrity is continuously assured, risks are proactively managed, and systems evolve through collective learning—delivering safer, more reliable, and more efficient operations across industries.
References
1. Digital Twin Consortium. 2020. “The definition of a digital twin.” Accessed 29 April 2026. https://www.digitaltwinconsortium.org/initiatives/the-definition-of-a-digital-twin/
2. National Academies of Sciences, Engi neering, and Medicine. 2024. “Summary.” In Foundational Research Gaps and Future Directions for Digital Twins. Washington, DC: National Academies Press. https://www.nationalacademies.org/read/26894/chapter/2
3. Singh, R., and J. Vrana. 2022. The World of NDE 4.0: Let the Journey Begin. ASNT.
4. Verdantix. 2021. “Best Practices: Imple menting Industrial Digital Twins.” Accessed 29 April 2026. https://www.verdantix.com/venture/report/best-practices-implementing-industrial-digital-twins
5. Heithoff, M., J. Michael, and B. Rumpe. 2024. “Digital Twin Maturity Model.” White paper. Software Engineering, RWTH Aachen University. https://www.se-rwth.de/essay/Digital-Twin-Maturity-Model/
6. Metcalfe, B., H. C. Boshuizen, J. Bulens, and J. J. Koehorst. “Digital Twin Maturity Levels: A Theoretical Framework for Defining Capabilities and Goals in the Life and Environmental Sciences.” F1000Research 12 (2023): 961. https://doi.org/10.12688/f1000research.137262.1
7. Belcic, I., and I. Smalley. 2026. “What Is the Digital Twin Maturity Model?” IBM Think. Accessed 29 April 2026. https://www.ibm.com/think/topics/digital-twin-maturity-model
8. Digital Twin Consortium. 2024. “Digital Twin Business Maturity Model.” White paper. https://www.digitaltwinconsortium.org/publications/digital-twin-business-maturity-model/
