## 3 Results and Performance Evaluation
### Experimental Setup and Data Acquisition  
AE signals were recorded from a real milling machine setup; the experimental setup is shown in Figure 10. The milling operations were conducted on an INTER-SIEG X1 Micro Mill Drill built from cast iron, which functions similarly to a small-scale pillar drill. The primary focus of this experiment was on straight parallel milling operations performed on steel workpieces, a process typically used for shaping and machining hard materials. The cutting tool used was a two-flute carbide end mill, worn to average flank wear of 0.3 mm, in alignment with ISO-8688-2 standards [37] for tool lifespan. The machining was conducted under the following conditions: motor speed of 1320 RPM (22 Hz), spindle speed of 660 RPM (11 Hz), and bed feet rate of 0.4 mm/s. These conditions ensure stable, consistent AE signal generation, capturing distinct fault signatures. Five steel pieces, each with dimensions of 20 mm, 35 mm, and 35 mm, were utilized during the experiment. The initial state of these workpieces is shown in Figure 11a, with a processed example displayed in Figure 11b. Figure 9: Feature importance: (**a**) distribution of features and (**b**) feature importance of 103 selected features after GA.  
were collected using the NI-9223 data acquisition system from National Instruments, with custom software developed by the Ulsan Industrial Artificial Intelligence Laboratory in Python 3.11. The AE data were collected at a high sampling frequency of 1 MHz, with each 1 s sample containing one million data points. Before the actual data acquisition began, the HSU-Nelson test was conducted to verify the proper functioning of the AE sensors. Both sensors successfully detected AE events during the test, confirming their readiness for the experiment. Two AE sensors were used in the experiment; the main sensor was fastened to the spindle, while a secondary sensor was attached to the motor. The sensor on the motor acted as a guard transducer, as depicted in Figure 10, which helped to filter out irrelevant signals and noise, ensuring that the primary sensor focused on collecting vital data related to the tool, bearing, and gear conditions. The data collection began under the normal operating conditions of the milling machine. According to ISO-8688-2 standards, a tool's lifespan is characterized by an average flank wear of 0.3 mm. However, in practice, tools may fail catastrophically even in the early stages of their life, especially when machining hard materials. To simulate these conditions, the tool, made of carbide, was intentionally worn to an average of 0.3 mm, and data were collected under these defective conditions. Additionally, an initial defect was introduced into the outer race of the bearing supporting the tool, and AE signals were recorded during machining. A small metal fragment was also removed from one of the gear teeth, which transmits torque from the motor to the spindle, creating a fault, and AE signals were similarly recorded during operation. In total, 100 samples were recorded for each operating condition, with simultaneous data acquisition on both channels to maintain data integrity and consistency across conditions. Table 3 provides an overview of the dataset taken from the milling machine. For ease of reference, the normal condition is labeled N, while the tool, bearing, and gear faults are labeled TF, BF, and GF, respectively. Table 4 provides a clear, high-level overview of the experimental setup and dataset for reference. A 1 s AE signal recorded under N, TF, BF, and GF conditions is depicted in Figure 12a-c. The AE frequency domain signals for different fault conditions (Bearing Fault sample, Gear Fault sample, Normal sample, and Tool Fault sample) are shown in Figure 13a-d. The faulty tool, bearing, and gear components used in the experiment are shown in Figure 14a-c.