earticle

영어

This research presents a GRNN(General regression neural network) approach for modeling the high temperature deformation flow behavior of 316L stainless steel under 800℃, 900℃ and 1000℃ and strain rates of 0.0002/s, 0.002/s and 0.02/s. There are many machine learning approaches of modeling the hot deformation of metallic alloys. Among them, the neural network approach is one of the most popular. However, the neural network approach takes a relatively long time and effort to compose and optimize the final model. In this research, GRNN is applied to study its applicability for modeling the hot deformation flow stress behavior. The prediction results were studied by calculating various types of error and observing the distribution of prediction error. The predicted results by the GRNN were very accurate and the GRNN was found to be highly applicable to modeling the flow stress of the hot deformation of 316L stainless steel.