## 1 Introduction  
The performance and reliability of alternators in vehicles are heavily dependent on effective thermal management strategies [1; 2; 3]. Alternators, responsible for converting mechanical energy from the engine into electrical energy, generate significant heat during operation, which can lead to thermal runaway and potential system failures if not properly addressed. Thermal management is a critical aspect of alternator design, as it ensures the efficient dissipation of heat generated by the alternator, thereby maintaining its optimal operating temperature and extending its lifespan. The fundamental principle behind alternator heatsink design is to maximize the surface area available for heat transfer, thereby enhancing the rate of heat dissipation. This can be achieved through the incorporation of extended surfaces, like fins, which increase the contact area between the heatsink and the surrounding air. Extensive research has been conducted to optimize the design of heatsinks, with a focus on improving their thermal performance and reducing the overall size and weight of the cooling system. Alternator heatsinks manufactured with a large surface area for better cooling are produced in a thin structure, and therefore, when subjected to dynamic loads, they can be broken from certain regions if their natural frequency coincides with the excitation frequency. For this reason, in the R&D studies of alternators, breakage can occur, especially in the heatsink bolt connection regions during vibration tests. This study investigates the prevention of fracture in the vibration fatigue testing of automotive alternator heatsink blocks using an artificial neural network. Automotive components, such as alternator heatsink blocks, are subjected to high-cycle vibration fatigue loads during their service life, which can lead to the formation and propagation of fatigue cracks and ultimately component failure. Artificial intelligence-based models are developed to predict the resonance frequency behavior of the heatsink blocks under vibration loading conditions. The results of this study demonstrate the effectiveness of using artificial neural networks in predicting the fatigue life and fracture behavior of automotive heatsink components, which can aid in the design and optimization of these critical thermal management systems [4]. Automotive components are subjected to various vibration loads during their service life, which can lead to the development of fatigue cracks and ultimately component failure. The fracture of materials subjected to fatigue loading is a prominent failure mechanism in engineering, accounting for up to 90% of all structural failures [5]. Accurately predicting the evolution of fatigue damage, including crack nucleation and growth, is crucial for the reliable design of automotive components. To address this challenge, researchers have explored the use of advanced computational techniques, such as artificial neural networks and regression analysis, to predict the fatigue life and fracture behavior of automotive components under vibration loading [6]. Artificial neural networks have shown promise in modeling the complex nonlinear relationships between various parameters that influence the fatigue life of engineering components. Prediction using neural networks is very effective and important in practice [7]. In this research, we present a methodology for using artificial neural networks and regression analysis to predict the fracture behavior of automotive alternator heatsink blocks subjected to vibration fatigue testing. The proposed approach first identifies the key parameters that influence the resonance frequency of the heatsink blocks, such as geometry and loading conditions. An artificial neural network is then trained to establish a functional relationship between these parameters and the onset of fatigue fracture [8].