## 2 Proposed Method for Fault Diagnosis in Milling Machines  
The proposed method involves converting AE signals into CWT scalograms, applying Gaussian filtering, using a CNN architecture with BiLSTM, enhancing it with GA, and following this with a fully connected layer for multiclass fault classification. The complete workflow of the proposed hybrid model is shown in Figure 1. The proposed method can be explained as follows.  
Step 1: This method starts with the preprocessing of AE signals from milling machines. AE signals are converted into CWT scalograms. This transformation allows us to capture both time and frequency information from the AE signals, which is critical for detecting transient patterns unique to each fault type. To improve the quality of the CWT images, a Gaussian filter is applied. This filtering process smooths the images, reduces the noise, and helps in improving the robustness of the feature extraction process.  
Step 2: After denoising, the filtered AE signals are processed through a CNN to extract high-level features. The VGG16 model, pre-trained on the ImageNet dataset, is utilized for  
Figure 1: The complete workflow of the proposed hybrid model for fault diagnosis.  
feature extraction. This model includes several convolutions and pooling layers designed to capture spatial features from the CWT scalograms. The convolution base of VGG16 is frozen to use the pre-trained weights and avoid retraining the entire model, which saves computational resources and time. Custom layers, including a flattened layer, are added on top of VGG16 to prepare for further processing. After extracting features using CNN, a BiLSTM network is added behind the CNN to capture temporal dependencies from the CNN-extracted features. The BiLSTM network processes the sequential data in both the forward and backward directions, obtaining long-range dependencies and enhancing the feature extraction capabilities. This integration addresses the problem of information wastage in the time series direction, which is important for accurately detecting faults in dynamic systems like milling machines. To further refine the extracted features from the BiLSTM output, GA is employed after the BiLSTM. The GA optimizes feature selection, reducing redundancy and focusing on the most informative features. For example, features associated with certain time-frequency patterns--such as transient bursts in tool faults or repeated impact signatures in bearing faults--are prioritized, allowing the model to identify key characteristics that distinguish each fault type. The process involves initializing a population of potential feature subsets, evaluating their performance using a fitness function, and applying selection, crossover, and mutation operations to evolve the population over several generations. This optimization approach improves the model's accuracy efficiency by ensuring that only the most useful features are fed into the final layers.  
Step 3: Finally, the obtained discriminant selected features are fed into a fully connected layer for fault classification. The proposed model is trained on the selected features using the training dataset. The model is compiled with the Adam optimizer and the categorical cross-entropy loss function. Training is conducted for several epochs, and the performance is monitored using validation accuracy. This layer assigns a fault class (e.g., Tool Fault, Bearing Fault, Gear Fault, or Normal) based on learned patterns, with high accuracy achieved through the combination of spatial, temporal, and optimized feature selection. The model achieves high validation accuracy, thus ensuring an effective approach for effective fault detection in milling machines. The final classification distinguishes among the four fault classes (Tool Fault, Bearing Fault, Gear Fault, and Normal) with high accuracy.