Determination of Decarburization Depth Base on Deep Learning Methods

Abstract

In the heat treatment of steel, decarburization is a serious issue that leads to poor wear resistance and low fatigue life. At present, the decarburization depth was determined using a visual estimation by the human eye, and the software estimation was determined through traditional image analysis. Therefore, decarburization depth analysis remains limited in experts and traditional algorithms. Artificial intelligence is a general-purpose technology that has a multitude of applications. This paper uses the concept of deep learning to propose a decarburization layer detector (DLD) that can determine the depth of decarburized layers. This DLD system boasts high performance, real-time, low learning, and computation costs. In addition, we used several kinds of decarburized layers images to compare the proposed method with other deep learning network architectures. The experimental results show that the proposed method yields a detection accuracy of 92.97%, which is higher than existing methods and boasts computational demands which are far lower than other network architectures. Therefore, we propose a novel system for automatic decarburization depth determination as an application for metallographic analysis.