# Prediction of Residual Stress of Carburized Steel Based on Machine Learning
###### Abstract  
In recent years, the number of machine learning applications (especially those involving deep learning) applied to predicting and discovering material properties has been increasing. This paper is based on using microstructure and carbon content to train machine learning models to predict the residual stress of carburized steel. First, a semantic segmentation model of the material organization structure (SegModel-MOS) was constructed based on the AlexNet network and initially trained on the PASCAL VOC2012 dataset. Then, the trained model was fine-tuned on an enhanced homemade dataset consisting of optical microstructures. The experimental results show that SegModel-MOS can distinguish acicular martensite, retained austenite, and lath martensite in microstructures. Finally, we used both support vector machine (SVM) and decision tree (DT) algorithms to establish a mapping relationship between the microstructure, carbon content, and residual stress to predict the residual stress of steel from its microstructure and carbon content. The experiments verified that the prediction model constructed in this study exhibits high accuracy and can directly predict residual stress without requiring any long-term measurements. Thus, the developed model provides a new approach to the study of residual stress in steel. residual stress; microstructure; semantic segmentation; support vector machine +
Footnote †: journal: Medical  
degradation of mechanical properties of Al2024-T3 aluminum alloy used in aircraft due to surface corrosion. However, both the chemical composition and microstructure of materials determine their mechanical properties. The above studies considered only the influence of chemical composition on mechanical properties; they did not consider the influence of organization on mechanical properties. To establish the relationship among chemical elements, microstructure, and mechanical properties, the microstructure must first be accurately identified. Thus, it is critical to be able to accurately segment photos of steel microstructures [9; 10; 11; 12; 13; 14; 15]. A neural network establishes a mapping from a feature space to a target attribute without considering any complex internal transformation laws; instead, it transforms the process into a set of trainable weights that can theoretically approximate any type of nonlinear transformation. Therefore, an increasing number of researchers have applied neural networks to various image segmentation tasks. In the metallurgical field, [PERSON] et al. [16] used a convolutional neural network (CNN) for segmentation to discover defects in steel and laid the foundation for deep learning to identify microstructures. Later, [PERSON] et al. [17] used an SVM to extract the morphological features of cast iron. [PERSON] et al. [18] used a random forest (RF) statistical algorithm to identify steel microstructures; their method can process large numbers of images rapidly. [PERSON] et al. [19] used a fully convolutional neural network (FCNN) to perform pixel-level segmentation of steel microstructures. These studies laid the foundation for the precise segmentation of microstructures. However, the above studies used convolutional neural networks exclusively to recognize photos acquired by scanning electron microscopy (SEM); they did not address recognizing complex optical photos. Therefore, this study used a fully convolutional neural network combined with transfer learning and residual blocks to semantically segment optical microscopy images [20; 21]. During this process, (1) transfer learning was used to solve the problem of insufficient network generalization ability due to fewer training samples, and (2) the residual block prevented overfitting. After the microstructure had been accurately segmented, an SVM classifier was used to predict the residual stress of carburized steel. According to [PERSON] et al. [22], the amount of retained austenite and the residual stress increase as the carbon content increases, and they decrease after tempering. [PERSON] et al.