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With the increasing competition of the manufacturing industry, it is essential for manufacturers to develop a new (or next)-generation product based on their prior product design, product user experience, historical performances, etc. The digital twin (DT) is believed to be a suitable technology to support product lifecycle management with excellent data capture and analysis capabilities and product family design capabilities. However, it remains a challenge to synthesize and incorporate the data and information captured from previous generational product development and stored on their individual digital twin instances (DTIs) into next-generation product design. To address this problem, this study proposes a new nominal digital twin (NDT) concept as a collective digital representation of the current generational product digital mockups (DMUs) and all individual DTIs of the built products in services for new-generational product development. NDT is first defined here as a prototypical and synthesized digital twin (or digital representation) of multiple individual digital twins corresponding to multiple physical products in use or previously used. By analyzing the data and information on their DTIs, NDT can enable the establishment and evolution of a more precise approximated model of many related family products used previously or currently in use in various application scenarios and environments in the physical world. This paper also demonstrates how a NDT model can be first established in the product design phase from various digital mockup models and enhanced later with a stochastic forest meta-model based on Bayesian optimization connected to DTIs. With this NDT model, collaborative exploration for optimal design solutions during new-generation product design and improvement can be performed on NDT through multi-objective optimization, which in turn can make new-generation product design easier and quicker. As a primary verification of the feasibility of our proposed approach, a case study has been carried out, and the results have well confirmed that the NDT-based new-generation product design approach is feasible. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data, Internet of Things, and other technologies, digital twin has gained great attention and become a current research topic. Using digital twin technology, the digital twin model can be constructed in the cyber space that is fully equivalent to the physical entity. It is always consistent with the physical entity in the operation process, which greatly improves the dynamic perception and prediction ability of the real world. After the development in recent years, digital twin has gradually changed from the initial concept discussion to the study of model framework and implementation method. However, because the research objects in different industries have great differences in their own composition, service conditions, and application scenarios, they have personalized characteristics in modeling strategies and usage methods. Therefore, based on different industries, this paper reviews the current articles on digital twins and distinguishes the focus of digital twin modeling research; subsequently, the relevant supporting techniques and methods are summarized according to their different importance for digital twin modeling. Based on the review in this paper, future researchers can conduct targeted research on digital twin technology in term of the characteristics of the objects in their industry. [ABSTRACT FROM AUTHOR]