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This study performed high-temperature compression tests at the temperature 900 to 1200 °C and strain rate 0.01 to 10 s−1 to characterize the high-temperature deformation behavior of AISI 4340. The constitutive equation of AISI 4340 was expressed using the Arrhenius model and the Zener–Hollomon (Z) parameter. Dynamic Recrystallization (DRX) behavior was evaluated by observing the compressed specimen with Electron Backscatter Diffraction (EBSD). The processing map is based on the dissipation efficiency of the dynamic material model (DMM) and the plastic instability criterion of Ziegler. At strain 0.4, the power dissipation efficiency value is 0.5 or more, and the instable zones are immediately identified through the processing map. The strain, strain rate and temperature data obtained from the FEM simulation of the hot forging process are displayed on the proposed 3D processing map to avoid the flow instability zones and ensure high power dissipation efficiency zones, allowing the operator to control the process’s temperature and speed.

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In this paper, a void closure model applicable to the general hot forming process has been proposed. Through the representative volume element (RVE) method, the influences of void shape, orientation, and stress state on void closure tendency were analysed. The void closure model was established so that it could consider these cross effects. The model calculates the changing void radius and orientation during deformation by considering the rate of change of the parameters affecting void deformation with respect to the effective strain. The model predicted the void closure tendency well on the RVE scale and predicted the void closure adequately in a multi-stage process with random voids. The results were compared with the stress-triaxiality-based (STB) model, which showed that the void closure model proposed in this study is applicable in general situations. A cogging process was analysed, and the degree of void closure at the end of each pass was compared with the calculated results of the void closure model. For the experimental verification of the proposed model, spherical and ellipsoid voids were placed in a rectangular specimen, and the radii of the voids after compression were measured. The measurement results were compared with the calculation results of the proposed model.

In this study, we propose a systematic process design method using a convolutional neural network (CNN) for the uniform strain distribution of a Ti-6242 impeller during forging. A convolutional neural network (CNN) is a machine learning algorithm optimized for processing grid-like data, such as images, by identifying patterns within the data. To achieve the design goal with a simple process, we propose a methodological process in which the initial billet passes through three steps: upsetting, preform forging, and target impeller forging. We used the CNN model in the upsetting and preforming steps to enable our proposed design method to be applied to various impeller shapes. We trained a CNN model with two different types of datasets: one to derive the preform shape suitable for the target impeller forging and another to determine the shape of the initial billet that was upset for impeller preform forging. The proposed forging process resulted in a reduction in the mean strain, strain standard deviation, and maximum strain by up to 38.6%, 52.5%, and 59.7%, respectively, compared with the impeller forging processes proposed in previous studies. Consequently, the strain of the forged product was been homogenized, thereby reducing the possibility of defects. This process design method can be used in fields such as aerospace that require high-quality forging.