# CNN-Based Ti-6242 Impeller Forging Process Design for Uniform Strain Distribution
## 3 Improved Forging Process Design Method  
### Overall Process Design  
In this study, an additional upsetting process was introduced to the initial billet before the preforming step. CNN model with a structure similar to Figure 3 was utilized to derive appropriate upsetting stroke for the preform shape. The CNN model was applied based on previous studies on preform design [41; 42; 43] for impeller-forging process. The model is based on the U-net structure [44] and comprises three input images: the final forged shape (\(x_{1}\)), the preform shape obtained through preforming (\(x_{2}\)), and the strain distribution after forging (\(x_{3}\)). The output image is the preform shape (\(y\)) for the beginning of the forging process. When predicting the preform shape for a specific forging product, the shapes of \(x_{1}\) and \(x_{2}\) are inputted the same to prevent underfill defects. Additionally, a zero tensor is inputted to \(x_{3}\) to aim for uniform strain distribution [25].  
The overall design process of the forging process is shown in Figure 4. The entire forging process consists of three steps: upsetting, preform forging, and target impeller forging. Two CNN models based on the structure of Figure 3 were used in this study. One was defined as Model I, which was trained to learn the shape of the upsetting billets as output given the preform shape as input. The other, Model II, was trained to learn the shape of the preform as output given the target impeller shape as input. Using the trained data, Model II can deduce the appropriate preform shape when the target impeller shape is given as input. Subsequently, Model I can deduce the appropriate upset shape when the deduced preform is given as input. Since the upset shape is typically different from the shape obtained by simply upsetting the billet, we proposed upsetting stroke design guidelines to determine the appropriate upsetting stroke. The details of the upsetting stroke design guidelines are described in Section 3.3. After determining the upsetting stroke, the initial billet is upset and forged into a preform. Finally, the target impeller forging is performed, completing the forging process.  
Figure 2: Result of: (**a**) Impeller I [32] strain; (**b**) Impeller II [30] strain; (**c**) Impeller III [35] strain; (**d**) strain range(\(\Delta\varepsilon\)), applying Method I [30; 32; 35] and Method II [25]. Strain concentration caused by two-step forging process can be crack propagation factor of Ti-6242 impeller.  
Figure 4: Schematic of impeller-forging process design. In this research, two separate CNN models are utilized to predict the preform shape of the workpiece before the forging process, as per the proposed technique. By inputting the shape of the target impeller, the appropriate upsetting stroke can be determined through deduction. Model I is trained using preform shape as input, upset shape as output. Model II is trained using target impeller shape as input, preform shape as output. Compared to existing design methods, the proposed approach increases the number of process steps by one.  
Figure 3: CNN U-net structure used in this study. Nineteen convolutional layers and eight pooling layers are used in U-net structure. Preform shape (\(y\)) I s derived as a result of 3 input images (\(x_{1},x_{2},x_{3}\)).