## 1 Introduction  
Tool wear is an inevitable phenomenon in machining processes [1, 2]. Intelligent manufacturing systems require accurate tool wear predictions to optimize production parameters, schedule maintenance activities, and ensure consistent product quality [3]. The phenomenon critically influences product quality, manufacturing efficiency, and operational costs [4; 5], with direct economic implications for industry. Unplanned tool failure may lead to increased manufacturing downtime, whereas accurate wear prediction enables just-in-time maintenance strategies that reduce tool replacement costs. Traditional approaches to tool wear analysis have primarily employed empirical or physics-based models, such as the Usui Adhesion Wear Model and Taylor's extended formula, which correlate tool life with cutting parameters. Although these classical formulations provide interpretable relationships, their effectiveness is limited by oversimplified assumptions [6]. These limitations reduce their applicability in modern machining scenarios, where dynamic interactions between tools, materials, and processes require more sophisticated modeling frameworks [7]. It is particularly evident in high-mix, low-volume production environments, where tool wear patterns vary significantly across different workpiece materials and cutting conditions. Consequently, traditional models often prove inadequate for real-time adaptive control. In recent years, machine learning has emerged as a promising alternative for tool wear prediction [8; 9; 10]. Methods such as neural networks and support vector regression demonstrate superior capability in capturing nonlinear patterns from operational data. Nevertheless, their \"black-box\" nature presents significant barriers to practical adoption [11]. The lack of interpretability in machine learning models hinders the identification of root causes underlying wear progression [12; 13]. Furthermore, these models typically require extensive datasets and computational resources while remaining susceptible to overfitting [14; 15]. This situation creates a critical gap between academic research and industrial implementation. Although deep learning models achieve high accuracy in controlled experiments, their deployment in factory settings is frequently impeded by the need for continuous recalibration and the inability to explain decision-making processes to plant engineers. Unlike conventional machine learning models, Kolmogorov-Arnold Networks (KANs) are a novel class of interpretable neural networks grounded in the Kolmogorov-Arnold representation theorem (KART) [16]. It inherently adopts a \"white-box\" structure with network layers that are designed as learnable univariate functions [17]. In essence, KANs approximate multivariate functions using an architecture based on KART, bridging the gap between interpretability and accuracy. This dual advantage addresses two major challenges in industrial AI adoption: the need for explainable models in tool condition monitoring and the ability to generalize across different machining scenarios without extensive retraining. Driven by the need for more interpretable and efficient neural networks, researchers have proposed some KAN variants and tested their effectiveness across diverse problems. For instance, [PERSON] et al. [18] introduced Convolutional Kolmogorov-Arnold Networks (CKANs), combining the foundational principles of KANs with convolutional mechanisms to achieve enhanced interpretability in intrusion detection. Similarly, [PERSON] [19] developed the Fractional Kolmogorov-Arnold Network (fKAN), demonstrating the versatility of KANs through the use of adaptive fractional-orthogonal Jacobi functions as basis functions. [PERSON] et al. [20] proposed SineKAN, which substitutes the commonly used B-spline activation functions with re-weighted sinusoidal functions. The significance of KANs is further evident in computational physics applications. [PERSON] et al.