# 3D Printing and Implementation of Digital Twins: Current Trends and Limitations
## 2 Current Digital Twin Applications in 3D Printing Technologies  
Current literature findings suggest that the compilation of successful digital twins to be applied in the various 3D printing technologies is feasible. With such cases being still relatively new, such applications face a number of challenges. The accurate digital representation of the process's hardware and surrounding environment, along with the initial software inputs and the amount of data from different sensors to be processed, possesses a complex workflow to be followed in order to come up with a functional DT [13]. [PERSON] et al. performed a first attempt to describe and fabricate a digital twin referring to a 3D printing process that utilized metal powder as raw material. It is described that there are simulation models capable of feeding a DT with inputs and that the combination of knowledge available in 3D printing and welding methods is preferable for making a digital twin than 3D printing techniques using metal as raw material [13]. The aspects of the process attempted to be addressed by the proposed DT are heat and material rheological simulation, solidification simulation, metal powder grain structure and texture, modeling of the material's porous microstructure and mechanical properties as well as residual stresses and subsequent potential distortion predictions. Their proposed DT are comprised of a combination of big data and ML, a statistical model, a control and sensing model, accompanied by a mechanistic model. [PERSON] et. al. proposed the development of a phenomenological framework, using the term digital twin, able to provide forecasts about the most vital parameters affecting the metallurgical properties of the 3D-printed items [52]. Per se the framework would give users the capability to provide various parameter settings to the 3D printer and, subsequently, to receive estimations about critical parameters such as transient temperature boundaries, molten pool geometrical characteristics, time and spatial alterations of cooling rates and solidification rates. Theoretically, the proposed digital twin of the 3D printing process, when sufficiently fed and validated with experimental data, would cause a shift from the current trial-and-error operating modus operandi to 'numerical experiments' that do not require pores of the physical world. On the other hand, [PERSON] et al. proposed their own case of a digital twin utilized for in situ real-time monitoring of the 3D printing fabrication process as well as material defect forecast [53]. In this case, this digital twin functionality was based on the inputs of various sensors accompanied by machine learning techniques and physical-model prognosis. It was tested in directed energy deposition (DED) and laser powder-bed fusion (LPBF) metal 3D printing techniques, showing promising results. In addition, [PERSON] proposed the use of the well-known grey-box modeling that is being used in various other fields as well [54; 55; 56; 57; 58]. A figure indicating a grey-box model is depicted in Figure 2. The model, interestingly not coined under the term of \"Digital Twin\", showed lower forecasting errors by utilizing the sensors' data obtained and performing relevant calculations [59]. The case referred to powder-bed fusion metal 3D Printing. A common feature of all the aforementioned proposed DT cases concerning additive manufacturing is their focus on metal 3D printing. This is mainly due to the fact that metal 3D printing is considered a 3D printing field characterized by elevated equipment and raw material costs. Therefore, a valid prediction of the process outcome in qualitative terms features a profound economy-technical benefit. However, the application of digital twins to 3D printing techniques such as FDM and SLA would be of great value due to their widespread use, regardless of the lower costs that these techniques share. In this direction, [PERSON] et al. proposed a digital twin case that focuses on 3D printing techniques using polymeric materials.