## 2 Wireless Sensors for Industrial Manufacturing
### Tool Wear Detection  
In a milling process, the quality of the tool determines the quality of the final surface finish of the product. However, the tool tends to wear down during the process, requiring additional work to reach the desired results. In some cases, tool chipping or breakage occurs, creating undesirable surface defects that are hard to restore. These tool chippingsarise due to factors such as excessive load or forced action on the tool's thermal crackdown during the process. These factors are unpredictable and need to be monitored. In rare instances, a tool may collide with the workpiece, damaging both. These tool collisions may occur due to a variety of reasons, such as deviation from the software model and the actual geometry of the workpiece and improper locking of the tool or the workpiece.  
To prevent these disturbances from occurring during the production process, the machine controller application must continuously monitor the condition of the tool in real time and react to the condition change by adapting process parameters such as the spindle speed or exchanging the tool before it is worn out. A vision-based analysis is impractical since the milling application is complex, with the tool mounted with the spindle. The other option is to analyze the Acoustic Emissions generated by the tool interacting with the workpiece. These acoustic waves differ from regular vibration waves with a higher frequency range, from 10 kHz to 900 kHz. The acoustic wave induced by the tool has a spectral characteristic that differs depending on the condition of the tool. Thus, based on the changes in the spectral characteristics, it is possible to detect the condition of the tool. These waves are sensed by the so-called Acoustic Emission sensors, which are integrated directly into the workpiece, as shown in Figure 1.  
The analog data are then sampled at very high frequencies, typically around 1 MHz, generating large amounts of data. These data are pre-processed for the spectral component and transmitted as a data packet. These data packets must be reliably transmitted in real time as the data packets contain the critical tool condition for the application controller to react to the changes. Due to the complex nature of the manufacturing process, with many movable parts, a wired solution using cables is not practical here since the cable needs to be considered in the process planning, increasing complexity and slowing it down. The 5G-enabled smart sensor and control with 5G are shown in Figure 2.  
A smart sensor platform with a high-speed analog-to-digital converter (ADC) is used to digitalize the AE sensor signals that are to be transmitted. These digitalized data are then preprocessed within the Field Programmable Gate Array (FPGA) to extract the necessary features, such as the Frequency spectrum of the detected wave, while simultaneously providing data suppression. The extracted features are packed in a UDP data packet and  
Figure 1: A smart sensor is attached to a turbine blade integrated disk during the milling process. In the center of the figure, the turbine blade can be seen, and in the top right corner, the sensor box with the electronics and the sensor connected to it can be seen.  
transmitted to the machine controller. The machine controller comprises an edge device and a PLC. The data from the sensor are processed with respect to the machine data in the edge devices. Based on the analysis, control signals are generated and sent to the PLC. The PLC then controls the machine, creating a closed-loop system from the sensor to the machine.