# CNN-Based Ti-6242 Impeller Forging Process Design for Uniform Strain Distribution
## 1 Introduction  
Ti-6242 is known to have excellent mechanical strength, stability, and creep resistance at high temperatures [1, 2]. Because of this, Ti-6242 is widely used in various fields, such as aerospace, medical equipment, nuclear power plants, and automobile structures. Ti-6242 is widely used as a material for parts that require high strength and durability [3]. Complex-shaped parts, such as impellers, have mechanical properties, such as high strength, that make it difficult to process the parts, leading to problems such as uneven blade thickness and narrow spaces between blades [4]. Several studies have been conducted to overcome the irregularity of Ti-6242 in the forging process and achieve a high final quality. [PERSON] and [PERSON] [5] investigated the hot die forging of 6 Al-4V Ti alloys and demonstrated several boundary conditions to achieve blade forging. [PERSON] and [PERSON] [6] presented a new beta forging process and confirmed that when a titanium alloy was heated at about 15 \({}^{\circ}\)C under beta transus, its composite properties could be improved. [PERSON] [7] proposed a new method to model the material behavior of titanium alloys during thermal deformation. Since the above studies have concentrated on specific conditions and shapes, there is a limitation that these results are difficult to apply when Ti-6242 is forged into various shapes. To overcome these limitations, several studies have been performed on forging a wider range of shapes by designing preform shapes during the forging process. Previous preform design processes have generally been performed directly by engineers aftertrial and error with experimental data [8]. Therefore, the design variables of the preform have had the disadvantage of being derived from the engineers' previous experience. To address this, [PERSON] and [PERSON] [9] considered the die charging problem addressed in previous work, the derived preform design equation, and the automated design process in axisymmetric forging. [PERSON] and [PERSON] [10] designed experimental, data-based equations for designing axisymmetric forging, especially the height and corner radius of the preform. In addition, a method for designing a preform for target geometry through simulation-based sensitivity analysis (SA) has been proposed [11; 12; 13]. In addition, some knowledge-based systems [14; 15], professional systems [16; 17], and backward tracking approaches (BTAs) [18; 19; 20] have been proposed to automate the preform design process in axisymmetric forging. However, the proposed methods are still limited in application because they are limited to shapes that can be analyzed via design variables. Evolutionary structural optimization (ESO) was introduced to eliminate components that did not satisfy the forging process design criteria during pre-design, to address the limitations of shapes available for preform design [21; 22; 23]. However, ESO is user-dependent; a method of using an artificial neural network (ANN) to address user dependence and improve efficiency has been introduced in some studies. [PERSON] et al. [24] used an ANN to automatically derive the preform design by connecting the forged product and the preform. Nevertheless, ANN was unable to create an appropriate preform shape due to the definition of weights defined as one-to-one connections. Therefore, a preform shape deduction using a convolutional neural network (CNN) has been presented to address the limitations of weights over one-to-one connections [25; 26]. CNNs construct several kernels and weight arrays and perform convolutional operations by searching for geometric features in the input array [27]. In previous studies, two methods have been used to forge the impeller; these are forging the billet directly into the target impeller [28; 29] and processing the billet in two steps: the process of preform forging and forging the impeller to improve the quality of the forging.