## 2 Related Review Studies  
[PERSON] et al. [17] classified current publications in the manufacturing industry based on the DT integration level. The authors showed a distinction between Digital Shadow, (DS), Digital Model (DM) and Digital Twin (DT). Additionally, they concluded that literature related to DT is scarce, whereas literature relating to DS and DM is abundant. Following the study by [PERSON] et al. [17], [PERSON] et al. [18] attempted to identify the missing part between the implemented and theoretical DT, including the integration level of the manufacturing execution system (MES). This review identifies different applications in the manufacturing domain and DT development in a laboratory to overcome the identified gap. The connection between these two publications [17; 18] is that they both focused on the DT integration level and the missing part in the current research. Finding the missing part in the current study is a popular track. However, the overview, definition, and application of characteristics to create a common base have also been of interest to the scientific community [16; 19]. [PERSON] et al. [19] defined DT as an artificially intelligent virtual replica of a physical entity. The review results provide an overview of definitions, characteristics and applications of DT based on scientific publications published before July 2019 in several application areas. [PERSON] et al. [16] attempted to consolidate DT research studies in the manufacturing industry by analysing 92 research studies to create a common base for future researchers. Thirteen characteristics, including physical entity/twin, virtual entity/twin, physical environment, virtual environment, state, realisation, metrology, twinning, twinning rate, and physical-to-virtual connection/twinning, were analysed to create consolidated knowledge. [PERSON] et al. [20] discussed sustainability and the associated technologies of intelligent manufacturing. Certain review criteria include intelligent manufacturing equipment, systems, and services. The applications reviewed in this study were  
* DT in product design,
* Manufacturing,
* Product service,
* Digital twin-driven sustainable intelligent manufacturing etc. Figure 1: DT in manufacturing. Network graph of keywords. Each keyword is represented with a circle and connections between keywords with connecting lines. A framework for digital-twin-driven sustainable intelligent manufacturing is proposed in this study. [PERSON] et al. [21] identified and classified 240 academic publications to identify the concepts, technologies, industrial applications, research status, and key enabling technologies associated with DT. Based on this analysis, the authors showed different lifecycle phases as future recommendations. Similarly to [PERSON] et al. [20], [PERSON] et al. [21] considered the design phase, manufacturing phase, concept, technology, application, digital model, and so on. According to the authors, the Digital Twin is stepping out of its infancy with regard to a widely accepted definition, unified creation, and deployment. The difference between the studies by [PERSON] et al. [21] and [PERSON] et al. [20] is that [PERSON] et al. considered concepts, technologies, industrial applications, research status, and key enabling technologies associated with DT, whereas [PERSON] et al. reviewed intelligent manufacturing equipment, systems, and services. A framework for digital-twin-driven sustainable intelligent manufacturing was proposed by [PERSON] et al., while [PERSON] et al. presented different lifecycle phases as future recommendations. [PERSON] et al. [1] reviewed 117 studies on AI ML-based DT. This review provides an overview of the standards and technologies used to create a DT relationship and of AI-ML, big data, IoT, digital twinning, related applications, the role of AI-ML and big data in DT, the tools needed to create AI-enabled DT, challenges, and future directions of digital twinning.