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Digital Twin (DT) is a concept of growing interest, driven by the technological advancements related to Industry 4.0. DT combines innovative technologies to create a virtual model replicating the physical system and allowing bi-directional data flow. However, to use DTs in support of decision-making, it is essential to have trust in the model and all components of the DT throughout its development. Verification and Validation (V&V) have traditionally been employed to assess models' credibility, providing a venue for the development of trust. Therefore, V&V are essential foundations for developing DTs that can support decisions in real-world environments. Through a systematic literature review, this research investigated whether and how researchers are employing V&V in DTs for manufacturing applications. It was concluded that very little research was reported to have performed both verification and validation of the developed DTs. The study also examined the most commonly used V&V techniques and explored their relationship with DT capability level and application areas. It was concluded that there is a lack of standard procedures to conduct V&V and a lack of agreement on the V&V objectives. This research uncovers the main challenges to verifying and validating DTs and future research directions to develop trusted DTs.
Bitencourt et al. (Wed,) studied this question.