## 6 IoT Systems Applied to Additive Manufacturing
### Comparative Analysis of Additive Manufacturing Applications  
The advancements in the IoT and additive manufacturing depict a transformative landscape in production processes, emphasizing the synergy between data analytics, real-time monitoring, and smart manufacturing techniques. Incorporating sophisticated measurement tools is paramount in continuously tracking crucial parameters during production of artifacts and assessing environmental factors at deployment sites. In parallel, the big data-driven sustainable smart additive manufacturing (BD-SSAM) framework focuses on improving product lifecycle management, highlighting how the use of data can contribute to reducing energy consumption, improving product quality, decreasing pollution levels, and increasing overall efficiency. This approach emphasizes the potential of data-driven decision making to achieve significant sustainability benefits in the manufacturing sector.  
The integration of IoT and LPWAN technologies in additive manufacturing has demonstrated how using sensor objects, realized by 3D printing, can generate smart products. These sensors play a crucial role in continuously monitoring production parameters and evaluating real-world performance once installed. It turns out that effective long-range data transmission and extended battery life are key to maintaining operational efficiency and reliability in additive manufacturing environments. In addition, an integrated process chain for additive metal production was proposed, highlighting the need for the full digitization and interconnection of production assets. Real-time monitoring is essential to facilitate continuous process optimization, guarantee product quality, and ensure equipment traceability. This digitization is essential for data acquisition and analysis, leading to informed corrective decisions during production.  
An innovative aspect is the application of digital twins in additive manufacturing optimization. Through IoT, AI, and virtual modeling, DT enables the simulation and prediction of production outcomes and facilitates proactive maintenance and efficient resource optimization. The integration of DT with 3D printing is proving to be effective in counteracting production defects through real-time adjustments and machine learning tools to improve parameter control. Finally, significant progress has been observed in DT application in designing and verifying biomedical scaffolds, highlighting how additive manufacturing can tackle complex projects and offer economic advantages over traditional methods. The collaborative potential of cloud and edge computing emerges as a key factor in enabling remote monitoring and efficient data transfer in 3D printing processes. The convergence of IoT, big data, and digital twin technologies within additive manufacturing heralds a new era of production methods characterized by increased agility, improved sustainability, and heightened cost-effectiveness. As these technologies continue to evolve, they promise to redefine manufacturing paradigms, paving the way for enhanced operational capabilities and innovation across various industries. The ongoing advancements in these fields exemplify the potential for smarter manufacturing solutions and underline the importance of harnessing data and technology for long-term industrial growth and sustainability. Table 6 summarizes the main topics of the analyzed articles indicating the technological readiness of the developed application.  
Nowadays, the constant and precise monitoring of the main AM parameters is essential. Monitoring the printing parameters, combined with the data processing provided by ML algorithms and DT models, allows for an optimized manufacturing process management. This manufacturing paradigm involves the production of higher-quality products, an essential aspect of market competition. With the quality of the product, another factor of interest is the minimization of costs linked to the reduction of waste and a greater production speed.