# 3D Printing and Implementation of Digital Twins: Current Trends and Limitations
## 1 Introduction
Ideally, the digital twin should be able to represent with realism, accuracy and fidelity its natural twin, i.e., the physical object, and in real-time to depict the state of the physical object. For this to happen, the use and integration of innovative technologies is required, some of which are in fact the \"pillars\" of the fourth industrial revolution (Industry 4.0). These are depicted in Figure 1.  
The Internet of Things (IoT) can certainly be considered a prerequisite for digital twins because, through wireless connectivity between different objects in the same or different physical parts, real-time data acquisition can be achieved. Then the sensors, as a component of tiny and low-energy embedded systems, are integrated into critical points of the physical object, so that the digital duo is constantly updated with valid data from the real physical system to its actual operating environment (in situ). Also, the data networks ensure the smooth exchange of data (measurements and commands) between the physical and digital twins and are usually in different geographical locations along with cloud computing. This includes all the tools and techniques for developing applications in the cloud computing online environment so as to maximize the potential for the inclusion of multiple and heterogeneous human and artificial resources. Other pillar technologies are simulation and emulation, which are well-known technologies whose methods have recently been rapidly evolving, utilizing all technological developments in areas such as computer aided design (CAD), data analytics and big data analytics. In addition, data visualization, through graphics technologies (computer graphics), virtual  
Figure 1: Digital twin pillar technologies. reality (VR) and augmented reality (augmented reality (AR)), exhibits maximum realism to the representation of the physical object within the virtual cyberspace. Moreover, artificial intelligence (AI), through its technologies for mechanical and deep learning (machine learning (ML), deep learning (DL)) allows the digital twin to self-train (self-leaning, self-training) and adapt the mathematical-computational model to the real physical model almost in real time. Digital twins incorporate the aforementioned technologies in order to tackle issues often faced in 3D printing processes. In the field of 3D printing, digital twins can be very useful towards leading to process parameter optimization by monitoring the process and detecting possible faults by utilizing in situ sensors' data as input. There is a constantly growing number of digital twins being used in various fields [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]; however, their use in the 3D printing process is still underway or limited. Digital twin technology can provide significant benefits in all areas of human activity, e.g., in the livestock sector, in shipping, in the automotive industry and in industrial production. The capabilities of digital twins can be applied to improve the design, management, maintenance, development and generally all industrial activities related to products, services, equipment, processes and functions as well as human resources. Indicative applications can be the design of new products based on the \"experience\" of all involved, but also the deep knowledge of the properties of the products, product lifecycle management (PLM), forecast of the occurrence of damages in production equipment, valid and timely preventive equipment maintenance, diagnosis of production problems causes, analysis of work experience as well as hyperautomation and support for robotic process automation (RPA). Siemens, among others, can be considered as one of the leading companies in this field, providing digital twin solutions for various industrial applications [50, 51]. The DT concept does not come without shortcomings and potential challenges.