## 1 Introduction
Additionally, artificial intelligence analysis is employed to develop a predictive model to detect the resonance frequency of the heatsink blocks, considering the identified critical parameters. The numerical implementation of this methodology leverages an efficient quasi-Newton monolithic solution strategy, which has been demonstrated to be effective in similar applications as reported in the literature [9]. The proposed approach aims to provide a comprehensive framework for the prevention of the fracture problem in vibration fatigue testing of automotive alternator heatsink blocks, ultimately contributing to the reliable design and performance of these critical components. Also, the developed and optimized methodology, which is integrated as a parameter estimation method into the optimization process, has been used in alternator development studies, which have an important place in the automotive product development process. The results obtained have been compared in terms of accuracy with conventional methods such as regression [10]. The fact that the developed learning-based method has shown higher accuracy compared to other methods and produced better results as a result of optimization has both proven the success of the method in the alternator local resonance frequency estimation process and eliminated the experience-based calculation and design suggestions in the alternator design process, shortening the calculation times and introducing a new approach to the literature. As a result, the development of AI models to predict the natural frequencies of vehicle components can provide significant advantages in addressing resonance-related issues and optimizing the design process [11; 12]. The following sections will provide information on the breakage of the heatsink that occurred due to local natural frequency in the vibration fatigue test conducted for the alternator. Simulation studies will be used to shift the natural frequency that caused this breakage to higher frequencies, and a data set will be created for the prediction methods to use. The grid search method was used to obtain the best artificial neural network model. The high-accuracy ANN model designed significantly reduced the computational time and effort [13; 14; 15; 16; 17; 18; 19]. Since optimization problems in the field of engineering require a lot of design modifications and these designs are costly in terms of both time and workload, surrogate models are frequently used to obtain objective and constraint functions. In this study, the artificial neural network method was used in the surrogate modeling phase due to its good results in engineering problems, such as ball bearings and electrical machines, which can be achieved using machine learning methods [20; 21; 22; 23]. In recent decades, many metaheuristic algorithms have been developed to improve the performance of the optimization studies to design better systems, and therefore, new optimization algorithms with enhanced search techniques have also been introduced to solve different optimization problems [24; 25]. As stated above, the results of optimization can change depending on the structure of an applied problem and also due to the search techniques' adaptability to the optimization algorithm according to the applied problem structure. Therefore, in this study, the new recent optimization algorithms are also used to see the effects of new optimization algorithms on the efficiency of the alternator heatsink design outcomes. In this section, each optimization method used in this paper is presented briefly. Only the fundamentals are presented, and readers who are interested by the optimization methods can see all the required knowledge in the cited paper. The arithmetic optimization algorithm (AOA) was introduced by [PERSON] et al. in 2021 [26]. The main inspiration for the presented algorithm was arithmetic operators such as division, multiplication, addition and subtraction, which are used in solving arithmetic problems.