## 6 IoT Systems Applied to Additive Manufacturing  
Additive manufacturing (AM) represents another important pillar of the Industry 4.0 paradigm. AM, also known as 3D Printing, is a contemporary technology that enables the creation of 3D items without traditional production (Figure 20). The process is called \"additive\" as it builds material layers one at a time to create final 3D objects [126]. Compared to traditional manufacturing techniques, AM offers the benefits of printing complicated forms, shorter production times, less waste, novel features, and cost-effectiveness. As a result, prototypes, meals, tissues, jewels, buildings, tools, and other items are being made using this rapidly developing technology. Since AM is a data-intensive manufacturing area, several ML techniques may be used to tackle problems with selecting, optimizing, monitoring, controlling, and designing AM process parameters. As a result, researchers' interest in the breadth of ML applications to tackle AM challenges is expanding quickly [127]. IoT encourages more customization, less material waste, and quick manufacturing in AM processes. The integration of 3D Printing with cyber-physical systems holds promise for manufacturing, design, and maintenance procedures. It also gives the user design freedom and the ability to perform multi-dimensional extrusion [128]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Monitored Process** & **Application** & **Monitored Parameters** & **Inferring Algorithm** & **Performance** \\ \hline [PERSON] & \multirow{4}{*}{Shop floor manufacturing process} & Production and quality control & Processing speed, idle time, parts per minute, and processed or & N.A. & N.A. \\ [PERSON] & & Predictive maintenance and quality assessment & N.A. & R4 RE & 3.76\% \\ M.S.S. & Water purification & \multirow{2}{*}{Anomaly detection conductivity} & Pressure and electrical conductivity & SVM (Approach 1) & 100\% \\ [PERSON] & & & & ANN (Approach 2) & (Approach 1) \\ \hline [PERSON] & \multirow{4}{*}{Monitoring of the transmission system of the condotte} & Lab-scale DCS & \multirow{4}{*}{Flow and pressure transportation system} & SCADA with NB-IoT & \multirow{4}{*}{26.2\% (C)} \\  & & & & \\ \cline{4-5} [PERSON] et al. [121] & \multirow{4}{*}{Implementation of a DT to improve the level of training, process control, and testing} & Automation of intelligent production systems & Simulation of real-life robotic cell behaviors & Machine learning (ML)-based dynamic scheduling agent digital engine & N.A. \\ [PERSON] & Optimization of operating cycles (OCPC) & Identification of time lags and errors in production & Operational cycle, completion time, and output efficiency metrics & Federated learning & 90.181 (\({}^{\text{\textregistered}}\))\% \\ \hline [PERSON] & \multirow{4}{*}{Monitoring of the steel and iron production cycle} & Product quality monitoring systems & N.A & SmartPQMS (data mining, ML, deep learning, and reinforcement learning) & N.A. \\ \cline{4-5}  & & & & \\ \cline{4-5}  & & & & technology) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of the references proposed in this paper relating to IoT-based process control solutions. Another tenet of the Industrial Internet of Things (IIoT) concept is additive manufacturing, where sophisticated measuring tools could be needed to track important parameters. At the same time, artifacts are being produced to assess the deployment site's environmental indicators. [PERSON] et al., in their work, consider the application of IIoT low-power wide-area network (LPWAN) technologies in an experimental setup of additive manufacturing [129]. Their research led to the realization of different objects by designing a powder bed 3D printer. These objects were sensorized at the start of the realization to monitor production process and next phase in the destination locations where such artifacts are inserted.