Do you trust digital twins? A framework to support the development of trusted digital twins through verification and validation
本文提出数字孪生V框架，将验证与确认活动融入数字孪生开发全生命周期，通过案例展示其应用，帮助从业者和研究者构建可信的数字孪生。
In recent years, Digital Twin (DT) has been used to design, control, and automate decision-making in various applications. Despite advancements in DT architecture, several challenges remain unexplored. For instance, to use DTs for decision-making, it is essential to trust all components of a DT. Model credibility, or model trust, is traditionally viewed from the perspective of Verification and Validation (V&V). Although V&V concepts are well-developed and widely applied in modeling, simulation, and other fields, such as systems and software engineering, there is very little mention of V&V procedures in the context of DTs. As DTs gain prominence in many application fields, developing DTs that can be trusted and used in real-world environments is essential. This study introduces the Digital Twin V (DTV) framework as a structured approach to building trusted DTs. The DTV integrates V&V activities across the DT development lifecycle, emphasizing iterative and recursive processes tailored to the unique characteristics of DTs. A case study applying the DTV framework to a fused deposition modeling additive machine illustrates its applicability. The framework’s scalability and potential for adaptation across various DT maturity levels are demonstrated, offering actionable guidelines for practitioners and researchers to enhance the trust and adoption of DTs.