## 1 Introduction
### Related Literature
AEH in the milling process arises from multiple sources, such as machine vibrations, tool interactions, and environmental noise, making it challenging to isolate AEH features for accurate fault identification. To overcome these challenges, this study uses a new approach. First, the AE signals are transformed into continuous wavelet transform (CWT) scalograms, which provide a rich time-frequency representation of the AE signals, extracting both the transient and non-stationary characteristics of the faults. The presence of noise in AE signals can introduce uncertainty and reduce the reliability of the fault diagnosis. To reduce this, Gaussian filtering is applied to the scalograms, which smooths the images and reduces noise while retaining essential fault-related data [28]. In recent years, deep learning techniques have outperformed traditional machine learning approaches due to their ability to independently learn discriminative features from raw data, without the need for manual feature engineering [29]. Among the most widely used deep learning models for defect identification are convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and deep belief networks (DBNs). CNNs are highly effective in extracting spatial features from data by using local receptive fields and shared weights. These properties reduce computational complexity and minimize the risk of overfitting. CNNs have already shown good results in diagnosing faults in mechanical components such as bearings, gears, and cutting tools [30]. However, while CNNs are effective in spatial feature extraction, they are less capable of capturing the temporal dependencies inherent in sequential data, such as AE signals. To address this limitation, the proposed hybrid model combines a CNN with an LSTM network. The hybrid approach allows the model to utilize the strengths of both CNN for spatial feature extraction and LSTM for capturing long-range dependencies in time-series data, making it particularly well-suited for analyzing AE signals collected during milling operations. In the proposed approach, the enhanced CWT scalograms are fed into a hybrid deep learning model, which consists of a CNN, a BiLSTM, a GA, and a fully connected layer. The CNN extracts high-level patterns from the denoised scalograms, while the BiLSTM captures the temporal dependencies in AE signals. Additionally, a GA is added to optimize feature selection, reduce redundancy, and improve classification accuracy. The fully connected layer combines the optimized features from the hybrid model and performs the final classification of the fault classes in the milling machines. This approach is validated on AE data obtained from a milling machine, and the experimental results show that the proposed method achieves superior fault detection and classification performance compared to traditional methods. The main contributions of this paper can be summarized as follows. 1. A Gaussian filter is applied to enhance the energy color intensity variations across multiple scales and frequencies in AE scalograms. 2. A hybrid deep learning framework that combines enhanced AE scalograms with CNN-BiLSTM models and GA for feature optimization is proposed to enhance fault detection in milling machines. 3. The proposed approach is validated using AE data collected from a controlled laboratory milling machine testbed, demonstrating its effectiveness in detecting various fault conditions. The structure of this paper is organized as follows. Section 2 describes the methodology of the proposed approach. The findings are presented in Section 3. Finally, Section 4 offers the study's conclusions.