# A Review on Recent Trends and Applications of IoT in Additive Manufacturing
## 2 IOT-Enabled 3D Printing Applications
### Input Parameters Optimization  
Additive manufacturing enables layer-by-layer deposition to produce three-dimensional things mainly depending on process parameters. Irrespective of the applications, the materials, shape, and quantity of the 3D printing parameters decide the quality of deposited output and thereby decide the material's properties. Wire arc additive manufacturing (WAAM) generates all AM techniques' highest deposition rate and arc energy [86; 87]. Hence optimizing and predicting the parameters is essential and offers certain advantages, such as being cost-effective, and time-efficient, and solving complicated designs, especially for large-scale components. In addition, it is crucial to minimize the material waste that enables lean production today. Some of the major developments after interconnecting the IoT with AM process parameters are process selection, material smart selection, post-processing, non-destructive testing applications, etc. Each metal additively manufactured object has its own set of specifications. Choosing process parameters to match these product specifications is made difficult by the vast number of process parameters available. Moreover, to achieve objectives such as geometrical requirements, good surface finish, improved mechanical properties, processing speed, etc., parameter optimization is required. In addition, the empirical method is commonly used in the industrial sector to reduce experimental effort [86]. A comprehensive investigation of the bead geometry constituting the layers is required to predict improved dimensional control of components. In order to better appreciate optimal geometry, researchers have employed various modeling and optimization techniques to merge many goals functions into a single objective function [88; 89]. In order to acquire the ideal set of parameters, the combined purpose of utilizing grey-relational analysis (GRA) is therefore crucial. Researchers nowadays implement modeling using AI techniques. For instance, as shown in Figure 16, wire arc additive manufacturing has various phases in its process. When the IoT is coupled with the WAAM process, step process accuracy will be possible. Some researchers, such as [PERSON] et al., have predicted the shape of deposited weld beads using neuro-fuzzy modeling of the machine learning technique and have produced high-quality, multi-layered WAAM deposits [90]. This study shows the importance of the IoT in any AM process. The IoT helps AM by monitoring product quality and identifying defects that help to improve the 3D printing process [91]. IoT-based additive manufacturing helps in a defect detection system, for instance, by providing novel insights into AM processes by identifying implicit knowledge and establishing a link between input parameters and output quality to ensure defect-free printing [92]. IoT-based machine learning algorithms help solve such types of challenges effectively. [PERSON] et al. [93] argue that such type of defect detection system for laser-based AM processes aids in ensuring product quality and improving printing process efficiency. Moreover, anticipating deposition process parameters improves deposition quality. [PERSON] et al. have identified that the output expectations of deposition depend on the input training data [94]. In addition, [PERSON] has investigated such defect detection and prediction modeling strategies that aid in the prediction of defects during 3D printing [95]. Using the IoT, the data from the defect analysis of actual deposition plays a key part; thus, thorough observations and correct input recommendations are required. Figure 16: WAAM process phases. Studying the functionality of materials that are new to the AM field also requires lots of research before going into industrial usage.