## 2 Related Work  
Collaboration is vital to perform complex tasks efficiently and collaboratively, while a single user cannot do it [4]. The authors of Reference [5] discussed homogeneous and heterogeneous multi-robot collaboration to perform their complex task in a decentralized fashion with blockchain technology. The authors of Reference [6] addressed the importance of machine learning of multi-robot cooperation by keeping connectivity and improving mobility in performing tasks. The authors of Reference [7] discussed the collaboration between drone and IoT devices for improving Industry 4.0 applications, such as smart city, smart healthcare, etc. However, not one of the above studies discussed how DTs can collaborate to improve Industry 4.0 application. However, we discussed the collaboration concept in DTs based on the above studies, and we discussed wind turbines as used case for Energy 4.0 of Industry 4.0. Smart manufacturing depends on the vast amounts of data collected by sensors and/or their DTs and the production line's data. These data could be erratic sensors and/or their DTs producing and injecting incorrect data in analysis, affecting decision-making. We will present a brief review of the state-of-the-art studies considering two main research areas: the role of DT for both data modeling and data analytics in manufacturing systems. Wind power 4.0 is one of the renewable energy technology that requires monitoring, delivering, and analyzing. The authors of Reference [8] presented DT monitoring that can be used for monitoring and development of wind farms. For fault diagnosis, the authors of Reference [9] discussed an approach to identify the network physical Vulnerabilities in Industry 4.0 systems systematically. It shows the potential impact of identifying vulnerabilities in Industry 4.0. Moreover, the authors of Reference [10] introduced supervisory control and data acquisition method for prediction and fault diagnosis of wind turbines using artificial intelligence. By gathering and analyzing big data from smart manufacturing products, it could take the initiative to determine the products fault diagnosis and health monitoring [11]. Concerning DT and data modeling, many research works use semantic technologies to enhance DTs models. [PERSON] et al. have proposed OSIsoft PI system augmented with semantic models [12]. The OSIsoft PI system has targeted DTs to capture, process, analyze, and store real-time operational data within the oil and gas industries. On the other hand, [PERSON] et al. have introduced a simple mechanism of formalizing knowledge as DT models coming from sensors in industrial production lines [3]. Their proposed mechanism utilizes graph-based query language to extract features from data, infer knowledge from large-scale production line data, and enhance manufacturing process management with reasoning capabilities. In addition, the authors of Reference [13] have introduced a conceptual architecture and model for smart manufacturing, relying on service-based DTs. The proposed architecture has described how automatically DTs compose the corresponding physical processes, sharing some analogies with web service composition. In addition, [PERSON] et al. have proposed an advanced physics-based modeling approach for predictive maintenance using DTs [14]. The proposed approach has been presented in three phases to reduce the modeling efforts and provide a common framework for the modeling of different resources enabling DTs: (1) machine modeling, including modeling the dynamic behavior of the machine using a black box, grey box, and white box models; (2) virtual sensor modeling, data is gathered during the simulation; (3) modeling parameters definition, the modeling parameters are updated with a suitable frequency to ensure the DT of the machine. Regarding the role of DTs and data analytics, [PERSON] et al. have proposed a model-based machine predictive maintenance that comprises a plurality of DTs, and a simulation platform [15].