# A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network
## 5 Discussion  
As the creation of the training database is a time-consuming process, certain simplifications must be implemented, as performed in this study. Two-dimensional axisymmetric geometries were considered in the definition of the training database for both forging and preform shapes. For comparison, the 2D axisymmetric simulations required an average of 10 min each, whereas the 3D thermo-mechanical simulation for the piston head forging required approximately 2 h. For this reason, the ability to create a simplified, but still reasonable, training database is important to achieve accurate results without consuming excessive computational time. Moreover, the consideration of a cold-forging FEM can also reduce the computational time. Although the material flow resistance is strongly influenced by the forging temperature, this simplification does not alter the accuracy of the preform design because the material flow itself is independent of the processing temperature.  
As presented in Sections 3 and 4, the CNN sub-models automatically derived the 3D surface of the preform candidates by utilizing the trained filters. Considering the first validation phases, relevant for the application of the trained CNN sub-models to two additional axisymmetric forgings and a \(\frac{1}{4}\) plane-symmetric forging, the results of the FEM investigations demonstrated the capability of the proposed preform design approach in deriving a preform shape that allows a significant reduction in the forging load by at least 11.1%. Regarding the grinding tool-like forging, the comparison between the experimental and FEM results validated the 3D forging FEM model implementation strategy and showed an improvement in the preform shape compared to that proposed in the relevant reference study [26]. Regarding the investigation of the piston head forging described in Section 4, the utilization of the preform shape derived by CNN#3 resulted in a reduction in the forging load of 15.09% (FEM results) and 15.94% (experimental result), as well as in the wear depth reduction estimated at 16.05%. Hence, the possibility of designing individual preform shapes, regardless of the material, allows the user to choose among the derived shapes based on the maximum force criterion and considering possible constraints for the preform manufacturing process.  
Although the present study was mainly focused on forging load reduction, additional target parameters can be included as target functions for the training database definition. For instance, damage or residual stress minimization can be added as a criterion for the preform shape. However, the complexity of the resulting FEM model hinders its application to complex geometries, owing to the unreasonable amount of computational time required for the definition of the training database. This issue is critical for complex problem solutions by means of machine learning algorithms and requires more work in the future.  
Furthermore, the training phase in the current study was based solely on the results of numerical simulations; however, it can also be linked to engineers' experience by feeding previously validated sets of preforms and final shapes along with the FEM results. This combined approach is particularly interesting because the robustness of the training data can be further extended without requiring any additional computation time.