# CNN-Based Ti-6242 Impeller Forging Process Design for Uniform Strain Distribution
## 5 Conclusions  
In this study, a forging process consisting of three steps--upsetting, preform forging, and target impeller forging--was designed using a convolutional neural network (CNN) to attain a uniform distribution of strain in the forged product. * When applying the upsetting stroke determined by the upsetting stroke design guidelines for the initial billet, the maximum, average, and standard deviation of the strain of the forged target impeller product were minimized compared to other upsetting strokes where the guidelines were not applied. * A comparative analysis of the impellers from the original study and those produced using the proposed process showed a significant reduction in the maximum strain, average strain, and standard deviation across three different impeller shapes. This substantial reduction underscores the enhanced efficiency of the proposed forging process, highlighting its superior performance in reducing key strain parameters. The strain range has also been reduced accordingly; however, further modification of the model is essential to achieve a lower strain range and reduce the probability of fatigue crack formation. * The proposed impeller-forging process reduces the concentration of strain during forging, thereby reducing the possibility of defects compared to existing processes. Therefore, it is expected that the process design method will be used in fields such as aerospace, where high-quality forging is required. * The CNN model used in this study was unable to learn the strain rate changes caused by temperature and friction variations; therefore, we are planning to modify the model to address this issue. Figure 15: Upsetting strokes for Impeller design II determined by upsetting stroke design guidelines for (**a**) temperature changes, with friction coefficient set to 0.3; (**b**) friction coefficient changes, with temperature set to 900 \({}^{\circ}\)C.  
**Author Contributions:** Conceptualization, N.K. ; methodology, K.K. and Y.K. ; software, K.K. and Y.K. ; validation, K.K., Y.K., Y.J. and I.S. ; formal analysis, K.K. and Y.K. ; investigation, K.K. and Y.K. ; resources, Y.J., I.S. and N.K. ; data curation, K.K. and Y.K. ; writing--original draft preparation, K.K. ; writing--review and editing, K.K. and N.K. ; visualization, K.K. and Y.K. ; supervision, N.K. ; project administration, N.K. and Y.J. ; funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the Korea Research Institute for Defense Technology Planning and Advancement grant funded by the Defense Acquisition Program Administration (DAPA) (No. 20-107-E00-017-03) and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1F1A1066519). Not applicable. This research was carried out with the help of the 'HPC Support' Project, supported by the Ministry of Science, ICT, and NIPA of Korea. This support is gratefully acknowledged. The authors declare no conflict of interest. * [PERSON] et al. (2008) [PERSON]; [PERSON]; [PERSON] Influence of dynamic boundary conditions on preform design for deformation uniformity in backward simulation. _J. Mater. Process. Technol._**2008**, _197_, 255-260. [CrossRef]
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