###### Abstract  
The objective of this study was to develop an artificial neural network (ANN) model for predicting the tensile strength of friction stir welding (FSW) joints between dissimilar materials, with a particular focus on aluminum and copper, using cryogenic processes. The research addresses the challenges posed by differences in material properties and the complex nature of FSW, where traditional experimental methods are time-consuming and costly. FSW experiments were conducted under a variety of conditions, and the resulting temperature data were utilized as input for a heat transfer analysis. The maximum temperature and temperature gradient obtained from the analysis were employed as input variables for training the ANN. The ANN was optimized using the Hyperband tuner and validated against experimental results. The model successfully predicted tensile strength with an average error of 5.4%, demonstrating its potential for predicting mechanical properties under different welding conditions. This approach offers a more efficient and accurate method for optimizing FSW processes. f +
Footnote †: journal: Medical  
to their excellent thermal and electrical conductivity. In the automotive industry, copper and aluminum are utilized in automotive batteries due to the implementation of electric drive systems [13; 14; 15]. The inverter of an electric vehicle is a device that converts DC power from the battery into AC power to control the speed of the motor, and coolant circulates through the inverter housing to perform thermal management. Conventional inverter housing covers are made of aluminum, but they are being replaced with copper materials to increase efficiency. The joining of copper and aluminum is prone to defects due to differences in melting temperature, fluidity, and material properties, as well as the chemical composition of the materials [16; 17; 18]. In friction stir welding, the maximum temperature reached and the temperature gradient encountered during welding depend on the welding process conditions such as tool rotation speed, feed rate, tool geometry (shoulder diameter, pin pattern, pin length, pin diameter), and other factors. Consequently, the mechanical properties of the weld are also affected, including tensile strength and elongation [19; 20; 21; 22]. Experimental methods to determine the mechanical properties of welds by parameters are time-consuming and expensive. To solve this problem, many studies have been conducted to predict mechanical properties using machine learning, a type of artificial intelligence [23; 24]. Artificial neural network (ANN), a type of machine learning, is an algorithm that mimics the function of the human brain. Researchers who applied ANN to predict the mechanical properties of FSW built a neural network by setting various parameters as input variables and material properties such as elongation, tensile strength, and hardness as output variables. [PERSON] et al. [25] used tool rotation speed, welding speed, and axial force as input variables, and used RSM (Response Surface Model) and ANN to predict and compare the tensile strength of A356 Alloy. [PERSON] et al. [26] used tool rotation speed, welding speed, pin diameter, and shoulder diameter as input variables and applied the LM (Language Model) algorithm and ANN trained with the BFGS (Broyden-Fletcher-Goldfarb-Shanno) quasi-Newton algorithm to predict the tensile strength of AA1100 aluminum alloy. [PERSON] et al. [27] used rotation speed and welding speed as input variables, and built a feed forward ANN to predict tensile strength, yield strength, elongation, and hardness, and compared the actual experimental data and simulation results with the predicted results.