## 4 Conclusions  
This study introduced a fault detection method for milling machines by combining high-frequency AE detection with an advanced DL algorithm. The primary goal was to diagnose faults in critical components such as cutting tools, gears, and bearings with high accuracy and efficiency. To achieve this, a Gaussian filter was applied to CWT scalograms to remove noise, ensuring that feature extraction and classification were based on clean data. The use of a VGG16-based CNN and a BiLSTM network enhanced the model's ability to capture both spatial and temporal features, while the addition of a GA for feature selection further refined the model, resulting in an impressive 99.6% diagnostic accuracy.  
This approach provides a robust solution for predictive maintenance by reducing unplanned downtime and maintenance costs and enhancing the operational efficiency of milling machines. To adapt this model to real-time industrial monitoring, AE sensors should be installed on key machine components to continuously capture signals, which are then sent in real time to a processing unit. A Gaussian filter should be applied immediately to reduce noise, retaining essential features for accurate fault analysis. Using edge computing or industrial GPUs, our CNN-BiLSTM model can process the signals rapidly for real-time classification. The model can also be updated with new data to remain effective as machine conditions change, thus enhancing long-term reliability. An alert system would notify operators immediately upon fault detection, allowing for timely maintenance and minimizing downtime. This setup ensures that the model is suitable for real-world applications, providing both accuracy and responsiveness in monitoring machine health. In future work, the proposed hybrid model can be extended by incorporating additional sensor data, such as force and temperature signals, to improve the reliability of fault diagnosis in milling machines. Furthermore, expanding the dataset with a wider variety of fault types and operating conditions will help to refine the model's generalizability for real-time applications in industrial environments.  
Conceptualization, M.U., M.F.S., N.U. and J.-M.K.; methodology, M.U., M.F.S., N.U. and J.-M.K.; validation, M.U., M.F.S., N.U. and J.-M.K.; formal analysis, M.U., M.F.S., N.U. and J.-M.K.; resources, M.U., M.F.S., N.U. and J.-M.K.; writing--original draft preparation, M.U., M.F.S., N.U. and J.-M.K.; writing--review and editing, J.-M.K.; visualization, M.U., M.F.S. and J.-M.K.; project administration, J.-M.K.; funding acquisition, J.-M.K. All authors have read and agreed to the published version of the manuscript.  
These results were supported by the \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (MOE) (2021 RIS-003). This work was also supported by the Technology Innovation Program ('20023566', Development and Demonstration of Industrial IoT and AI-Based Process Facility Intelligence Support System in Small and Medium Manufacturing Sites') funded By the Ministry of Trade, Industry, and Energy (MOTIE, Korea).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Conflicts of Interest: Author [PERSON] is employed by PD Technology Cooperation. The remaining authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.