# Digital Twins for Additive Manufacturing: A State-of-the-Art Review
## 2 State of the Art of Digital Twin for Additive Manufacturing  
The concept of digital twins was firstly proposed by National Aeronautics and Space Administration (NASA) to monitor the behaviors of a satellite. Using this method, the possible changes in the settings could also be simulated. NASA wanted to explore the space using a digital twin replica of the physical system [17]. After that, various studies were undertaken to develop this technology. Till now, it has been constructed and utilized for different manufacturing processes by many industries and government agencies [15]. Recent studies and research indicate that construction of a first-generation digital twin of AM is achievable. However, this technology is still in its infancy, and facing various research challenges. The components required to construct a digital twin of AM hardware, software, and related technologies are still under development and much work is still needed to fulfil its full functions [3]. [PERSON], [PERSON] and their co-workers did lots of the pioneering work in this area [3; 15; 18]. They provided an overarching framework for the implementation, undertook lots of explorations to demonstrate this concept within the AM research community, and provided a perspective of the current status and research needed for the main building blocks of a first-generation digital twin of AM. In their research, they refer to the approach as the digital twin for AM and suggested that a digital twin of 3D printing [3; 18] consists of a mechanistic model, a sensing and control model, a statistical model, as well as big data and machine learning, as shown in Figure 1.  
[PERSON] et al. [18] presented a novel framework of a mechanistic model to predict the melt-pool level phenomena. The 3D curved surface deposit geometry for single-pass deposits, transient temperature and velocity distributions, cooling rates, solidification parameters, and secondary dendrite arm spacing and micro-hardness were accurately estimated by the proposed building blocks in a computationally efficient manner. The author called it blocks of a first-generation digital twin of AM. [PERSON] [19] provided gray-box modeling for a powder bed fusion AM process and demonstrated that it can lower predictive errors, as shown in Figure 2, the basic idea of [PERSON]'s team is to make predictions through calculation based on the data obtained, and gray-box modeling is the term used by them, not digital twin. [PERSON] et al. [16] demonstrated an early foray of the digital twin paradigm for real-time process monitoring and defect prediction. In this research, the combination of physics-driven predictions with in-situ sensor data and machine learning led to higher statistical fidelity in detecting process flaws. The experiments of laser powder bed fusion (LPBF) and directed energy deposition (DED) metal AM processes were both conducted. The results substantiated this conclusion. Figure 1: Logical representation of the digital twin for additive manufacturing (AM) [9; 15]. Figure 2: Gray-box model [19]. [PERSON] et al. [20] did the first work that demonstrated how dynamic data-driven application systems enabled a feature re-ranking method that can help in keeping the digital twin up-to-date, as shown in Figure 3. The key performance indicator is the surface texture of the product and dimension of the object, which is simple and easy to observe. This could be taken as a prediction of the components' \"shape\", and the target is in a plastic form, which is quite different from the additive manufacturing of metal components. All in all, the digital twin in additive manufacturing is a new concept, and the recent research has mainly focused on verifying the concept to predict the temperature distribution and thus to compute the defects that may occur and the relative properties such as the hardness and thermal strain [18].