# Performance Investigation of Coated Carbide Tools in
###### Abstract
Specifically, FEA enables the detailed study of stress distribution and thermal effects, as well as the tool-workpiece interactions under different machining conditions, providing critical insights into wear mechanisms such as abrasion, adhesion, and diffusion. By discretizing the tool and workpiece into smaller finite elements, the method allows researchers to simulate complex physical phenomena, including chip formation, heat generation, and material deformation [13]. Moreover, advanced material models and boundary conditions could also be incorporated in order to enhance the accuracy of simulations, allowing for realistic predictions of tool wear progression over time. Additionally, FEA facilitates the analysis of cutting forces, temperature gradients, and residual stresses, which are essential factors in determining tool longevity and machining efficiency. It is worth mentioning that even FEA is computationally intensive and requires precise material property data; it remains a valuable tool for optimizing milling operations, as it enables the prediction of tool performance under various machining conditions without the need for extensive experimental testing [14]. However, FEM simulations require extensive computational resources, precise material characterization, and accurate boundary conditions to achieve reliable predictions. As an alternative methodology, artificial intelligence (AI)-based approaches have emerged in the past years as effective tools for predictive analysis in machining operations. In particular, AI algorithms have become increasingly valuable in manufacturing research, particularly in predicting the performance and wear behavior in machining procedures [15]. AI techniques, such as artificial neural network (ANN), deep learning (DL), k-nearest neighbors (KNN), and support vector regression (SVR), could utilize experimental datasets in order to develop predictive models capable of estimating tool life, wear rate, and manufacturing efficiency based on key process parameters. These algorithms analyze vast amounts of data, identifying complex relationships between input parameters and tool wear that may not be easily captured by traditional empirical models [16]. Unlike FEA, which requires extensive material characterization and computational resources, AI models can rapidly process new data and adapt to different milling conditions with minimal computational cost. These models offer fast and accurate predictions with significantly lower processing effort compared to FEM simulations. Furthermore, machine learning (ML)-based approaches could enable real-time monitoring and optimization of machining processes, allowing manufacturers to implement data-driven decision-making for improving tool longevity and efficiency [17]. On the other hand, AI models require a large amount of data in order to identify patterns and correlations between input features, where the quality, quantity, and relevance of the data directly impact the model's performance. The objective of the present research work is to investigate the performance of coated carbide tools in milling operations by combining experimental, numerical, and AI-based methodologies. An experimental dataset was created in a controlled laboratory environment to examine the effects of varying machining conditions on tool wear. Additionally, AI models were developed to predict tool life based on this dataset, while FEM simulations were utilized to estimate the tool wear through numerical analysis of the milling process, as well as to simulate the perpendicular impact test on the examined coated tools. Finally, a comparative evaluation of AI-driven predictions and FEM simulations was conducted to assess the advantages and limitations of the employed methodologies. The findings of this study could contribute to the optimization of milling procedures by providing a data-driven approach towards the enchantment of tool performance, the reduction of wear, and the improvement of the overall manufacturing efficiency.