## 1 Introduction  
Tool wear is a naturally occurring phenomenon during cutting and machining, caused by the interaction of unavoidable chemical, mechanical, and physical factors. If this problem is not diagnosed and addressed promptly, it may lead to abnormal cutting conditions, potentially affecting machining efficiency and quality in serious cases [1, 2]. Implementing an effective tool condition monitoring system can improve surface quality, increase productivity, and reduce machining costs. [PERSON] et al. found that machining costs can be reduced by more than 30% [3, 4]. Currently, there are two main types of tool condition monitoring systems: direct and indirect systems. Although direct monitoring is highly accurate, it must be stopped during monitoring, making it cumbersome and complex, with little significance for actual processing. Indirect monitoring, on the other hand, utilizes multi-signal fusion technology, selecting various monitoring signals such as acoustic emission, vibration, and current signals. These signals are input into different models for comparison and analysis, which then output the corresponding tool wear state [3, 5]. In indirect monitoring, the choice of neural network models, such as support vector machines (SVMs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs), often influences the performance of the monitoring system. [PERSON] et al. [6] used sensors to collect cutting force signals from a machine tool during end milling, extracted features in the frequency, time, and time-frequency domains, and proposed a Whale optimization algorithm (WOA)-support vector machines (SVMs) model to analyze these features for monitoring tool wear. [PERSON] et al. [7] analyzed the vibration signals in both time and frequency domains and used LSTM networks in conjunction with the constructed time series dataset to analyze the wear of a single-edged hob. [PERSON] et al. [8] utilized a Bayesian approach combined with a convolutional neural network (CNN) to provide uncertainty sensing for a tool-monitoring system. In recent years, multisensor fusion has become increasingly important in the field of monitoring and identification, as it allows for the complementary use of information from multiple sensors [9; 10]. [PERSON] [9] utilized four key parameters--acoustic emission, vibration, power, and temperature--and combined them with deep multilayer perceptron, long short-term memory, and convolutional neural network techniques to effectively achieve real-time detection of tool status. In addition, [PERSON] [9] further developed a machine learning-based tool wear stage recognition system by acquiring cutting force, vibration, and acoustic emission signals through multisensor technology and applying information measurement techniques for signal feature extraction and dimensionality reduction. [PERSON] et al. [11] evaluated the prediction accuracy of vibration and force signal measurements using an artificial neural network model, verifying the relevance of the prediction results by comparing them with actual measurements. Recent studies have shown that while the use of multisensor fusion techniques improves the accuracy of monitoring and identification results, deeper network layers are often required to extract more complex features due to the increased dimensionality of the inputs from the additional monitoring signals [12; 13; 14]. Despite their ability to learn complex features, deep neural networks with excessive layers often suffer from vanishing or exploding gradients. To address this problem, [PERSON] [15] and others designed a network module containing a constant mapping, called a \"residual learning unit\", which allows certain layers in the network to be connected by skipping one or two layers directly. [PERSON] et al. [16] improved the prediction bias of tool wear by using a combination of a short-time Fourier transform and an improved deep residual network. [PERSON] et al.