# Performance Investigation of Coated Carbide Tools in
###### Abstract  
The optimization of the manufacturing conditions in milling processes composes a crucial task for enhancing machining efficiency and extending the tool's lifespan. This study presents an investigation of the cutting tool's performance under varying machining parameters via the generation of an experimental dataset that was obtained through laboratory-controlled milling operations. Based on this dataset, artificial intelligence (AI) models, including artificial neural network (ANN), k-nearest neighbors (KNN), and support vector regression (SVR), were developed in order to predict the tool's life as a function of the milling conditions. Additionally, finite element method (FEM) simulations were conducted to estimate tool wear and analyze the manufacturing process at a numerical level. In particular, FE models were utilized to compute the milling forces and the corresponding developed stress fields, as well as to assess the cutting tool's performance based on certain machining variables. Furthermore, a comparative analysis between AI-driven forecasts and FEM simulations was performed to evaluate their effectiveness and reliability. The findings provide insights into the advantages and limitations of both methodologies, guiding the optimization of coated carbide tool performance. The outcomes of this study contribute to the advancement of predictive modeling in machining processes, offering a data-driven approach for improved tool wear assessment. m m  
have enabled the machining of difficult-to-cut materials like titanium alloys, composites, and hardened steels [4]. Among the available tool materials, coated carbide tools have gained significant attention due to their superior hardness, wear resistance, and ability to withstand high temperatures [5]. These coatings are typically composed of materials such as titanium nitride (TiN), titanium aluminum nitride (TiAlN), aluminum chromium nitride (AlCrN), or chromium nitride (CrN) that provide additional protection against abrasive wear, adhesion, and thermal degradation [6]. Despite these advantages, the progressive wear of coated carbide tools remains a critical issue, affecting machining precision, surface finish, and the overall manufacturing costs [7]. In addition, the performance of coated carbide tools is influenced by a complex interplay of factors, including the cutting parameters, coating materials, and workpiece properties. Hereupon, a deep comprehension of the wear mechanisms of coated carbide tools is essential for improving machining efficiency, maximizing tool life, and reducing downtime due to frequent tool replacements [8]. In general, tool wear is related to several factors, including cutting speed, feed rate, depth of cut, workpiece material, tool material, coating, and cutting fluid, among others [7; 8]. The interplay between these parameters determines the extent of wear mechanisms such as abrasion, adhesion, and diffusion, which directly impact tool life and machining performance [9]. To study these effects, experimental approaches have been commonly used, where milling tests are conducted under different conditions to evaluate tool wear behavior. However, experimental investigations alone can be time-consuming and expensive, necessitating the development of predictive models to estimate tool life based on machining conditions [10]. Therefore, in the past decades, numerical approaches have developed in order to inspect the machining procedure and assess the performance of the applied milling operation [11]. Traditionally, the finite element method (FEM) has been employed to simulate machining processes and predict tool wear through numerical modeling. Finite element analysis (FEA) is a powerful numerical simulation technique widely utilized in machining research to investigate the performance and wear behavior of carbide tools in milling processes [12].