CNN-Based Ti-6242 Impeller Forging Process Design for Uniform Strain Distribution

Abstract

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.