# 3D Printing and Implementation of Digital Twins: Current Trends and Limitations
## 3 Practical Issues in Accordance with Procedure Needs  
### In Situ Monitoring  
Opposed to traditional subtractive technologies, where in situ monitoring technology is being commonly implemented, 3D printing techniques have not yet integrated monitoring sensors, apart from experimental cases. However, current literature findings suggest that new approaches are being tested that will eventually allow for the seamless integration of monitoring techniques and lead to the desired surface quality and mechanical properties standards.  
An accurate digital mapping and representation of the physical environment surrounding the 3D printer is highly desirable due to its immense role in the outcome of the process. This environment is highly dynamic and is also influenced by the human factor. Airstreams as well as humidity in the surrounding environment are known factors that can affect 3D printing items' overall quality [2]. Therefore, it needs to be constantly and accurately monitored for the digital twin to be able to predict these interactions. In this way, in situ process monitoring is based on the analysis of in situ acquired sensor data. Specific datasets are being subsequently linked to fabricated items' distortions and failures, geometries and specific process conditions using ML models [69, 70].  
Unfortunately, there are a number of drawbacks on utilizing solely data-driven approaches in order to perform the in-situ monitoring process. Firstly, a large number of completely different datasets derived from various sensors have to be acquired and processed. These can range from temperature and strain readings to high-rate imaging data. Thus, acquiring and processing of different data need to be combined with intra-system compatibility, the possibility of which remains questionable. In addition, the computational power for such an operation needs to be elevated, and in most cases desktop PCs are seldom up to this task.  
Secondly, monitored factors such as built item geometrical characteristics, deposition patterns, temperatures and deposition speeds are all linked to specific acquired signal patterns; thus, data driven models need to be retrained if any of these factors change. Obviously, this causes a great concern due to the fact that such a model will not be compatible with another 3D printing technology.  
Thirdly, ML models often require the acquisition of an elevated amount of input sensor data, which increases model complexity and may lead to prediction uncertainty. However, in the case of a digital twin to be utilized in the greater field of 3D printing technologies, a large amount of data is needed in order for the DT to be sufficiently trained and thereby for its long-term accuracy to be improved. Such data to be entered to the DT could be derived from real-world experiments, literature, various embedded sensors, or numerical and theoretical simulations. [PERSON] et al. [53] propose the combination of a theoretical model and a lower number of in situ sensor readings in order to reduce the computational burden as well as the amount of sensor data that need to be acquired and processed. Another suggestion could be that for each of the common 3D printing processes, there should be a predefined model for each different scenario (i.e., variety of raw materials, 3D printer environment, printer category, etc.). Thus, users would just need to input the data to use that targeted DT of their specific real-time fabrication process. However, further research is still needed to reach this point.