Microstructure Image Segmentation of 23crni3mo Steel Carburized Layer Based on a Deep Neural Network

Abstract

This paper identifies and analyzes the microstructure of a carburized layer by using a deep convolutional neural network, selecting different carburizing processes to conduct surface treatment on 23CrNi3Mo steel, collecting many metallographic pictures of the carburized layer based on laser confocal microscopy, and building a microstructure dataset (MCLD) database for training and testing. Five algorithms—a full convolutional network (FCN), U-Net, DeepLabv3+, pyramid scene parsing network (PSPNet), and image cascade network (ICNet)—are used to segment the self-built microstructural dataset (MCLD). By comparing the five deep learning algorithms, a neural network model suitable for the MCLD database is identified and optimized. The research results achieve recognition, segmentation, and statistic verification of metallographic microstructure images through a deep convolutional neural network. This approach can replace the high cost and complicated process of experimental testing of retained austenite and martensite. This new method is provided to identify and calculate the content of residual austenite and martensite in the carburized layer of low-carbon steel, which lays a theoretical foundation for optimizing the carburizing process.