# A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network
## 4 Experimental Verification  
To verify the applicability of the proposed preform design approach to a real industrial case, piston head forging (Figure 17) was considered. This forging component is a one-quarter plane-symmetric shape characterized by thin walls and is designed for weight reduction.  
The piston head geometry was input to the trained CNN sub-models and the derived preform shapes are shown in Figure 18 and Table 5. The derived preform shapes were utilized in the 3D thermo-mechanical FEM simulations, as described in Appendix B. As for the industrial manufacturing process, the preforming stage can prevent die failure by reducing the die wear depth, which is significantly related to the forging load. Therefore, for the piston head verification step, the die wear depth according to the preform shapes was also estimated by the FEM investigations. Regarding the preform shapes derived  
Figure 16: (**a**) The preform design from the literature [26], (**b**) preform design by CNN#5, and (**c**) experimental forging force-punch patch curves from the literature from FEM for the designed preforms by CNN sub-models.  
Figure 15: Preform shapes by CNN sub-models for the grinding tool-like shape.  
by CNNs #4, #5, and #6, some features were considered too complicated to be obtained during a preform-forging phase, resulting in folding defects during the piston-forging phase, as highlighted in Figure 18. Therefore, these three candidates were excluded from further investigation.  
As shown in Figure 19, the remaining three CNN-based preforms enabled the complete filling of the die, and the preform based on the CNN#3 model yielded the largest forging force reduction, estimated to be 15.09%, and promoted a wear depth reduction, estimated to be 16.05%, in comparison with the cylindrical billet.  
Based on the positive results obtained from the FEM investigation in Figure 19a, two experiments were performed considering the CNN#3 preform design and the cylindrical billet. For the hot forging process, the HKLP-800 press machine shown in Figure 20a was utilized, and the set-up conditions for the process summarized in Appendix B were utilized in the 3D FEM simulations. The top and bottom dies, relevant for the piston head shape, are shown in Figure 20b.  
Because the preform die has not been manufactured yet, the preform shape derived by CNN#3 (Figure 21a) was obtained by machining from a cylindrical billet. In addition, an experiment considering a cylindrical billet was performed for comparison. When comparing the experimental and numerical forging load-stroke curves for the cylindrical and CNN#3-based preforms, as shown in Figure 21c, the deviation between the experimental and FEM results was limited to 1.05% and 0.55% for the area beneath the curves and 1.95% and 0.93% for the case of the maximum load, respectively. These limited errors indicate that the implemented 3D thermo-mechanical FEM model can properly replicate the considered forging process. In addition, the cross-section of the forged piston head (Figure 21b), cut through computer numerical control machining, shows the high quality of the filling as well as the absence of any overlap of the metal flow. This result proves the reliability of the defined CNN-based preform design approach and the accuracy of the implemented 3D thermo-mechanical FEM model in replicating the considered forging process conditions.  
Figure 21: (**a**) Cylindrical billet and CNN#3-based preforms, (**b**) cross-section of the forged piston head with the CNN#3-based preform, and (**c**) forging force–punch patch curves obtained from experiments and FEM simulations.