## 3 Results
### The Data Monitoring of the DED Process on Irvar  
The data provided from the mold manufacturing process (Figure 7) proves to be invaluable in enhancing the overall efficiency and quality of the manufacturing process. The chosen geometry is a reference geometry for molds in the aeronautical sector, and has been proposed by companies in the sector. The signals have been acquired at a rate of one hertz, one data per second, as we are not looking for a dynamic reaction to the process, but for a change in the quasi-static state. The signals acquired are the same as in all the tests shown in this work. Through a meticulous analysis of this data, various trends and patterns emerge, shedding light on areas that require attention and improvement. For example, temperature data can identify the regions of the mold that experience higher temperatures, or where the heat concentrates, allowing for corrective actions to be taken in subsequent passes. Such insights may signify issues with the cooling system or point to inadequacies in the mold part design. This design has been used in previous works [34]. The feed rate data become a crucial metric in determining whether the wire feeder operates at the appropriate speed. This ensures the uniform solidification of the mold part, contributing to higher quality outcomes. The energy data play a pivotal role in assessing the wire feeder's energy consumption. Optimizing energy usage not only reduces production costs, but also enhances the overall process efficiency. Mold manufacturing process monitoring, facilitated by comprehensive data analysis, emerges as a powerful tool for refining efficiency, quality, and profitability. The insights gained from these data pave the way for continuous improvement throughout the various phases of the manufacturing process. In a three-dimensional context with Cartesian X, Y and Z axes in millimeters, the visual representation in Figure 7 is a three-dimensional graph showing the evolution of six key process parameters over time. Each of the collected variables is represented in a subplot, and the color in the graph w  
Figure 6: Zero bead morphology of the whole test performed in Table 3; the parabola-modelled geometry from the data collected via the profilometer. of the property at each three-dimensional point. In Figure 7, the variables represented are as follows: (a) the voltage which remains constant during most of the process; (b) the energy per unit length, which is higher in the lower layers when encountering the colder substrate, and then decreases in value; (c) the wire feed speed; (d) the transverse feed speed; (e) the current; and (f) the temperature which is raised in the area where it is higher in the straight section. Temperatures were measured using a pyrometer on the torch, capturing the surface temperatures of the bead at a given distance from the molten pool. This temperature data can be used to optimize the welding process in two key respects. First, it allows for the precise control of bead geometry via correlating temperature variations with the desired shape; at lower temperatures, a narrower and taller bead is estimated, while at higher temperatures, a wider and shorter bead is estimated. Secondly, the data help to detect defects by identifying cold areas, which indicated a lack of fusion, and high temperatures, which indicate possible porosity due to gas accumulation. This early detection of defects allows corrective action to be taken in time, thus ensuring the production of high-quality beads. As this is not a direct melt pool measurement, the correlations and measurements are not reliable, but are expected to be effective in combination with the other measured parameters and through artificial intelligence modelling. Figure 7: Data collected from the numerical control of the machine and sensor: (a) voltage, (b) energy, (c) wire feed speed, (d) traverse speed, (e) current, and (f) temperature. Utilizing the data to identify trends and patterns can help predict potential issues.