**1. Introduction**

High-carbon steel wire rods are the main raw materials for the production of spring steel, prestressed steel strands, steel cords, steel wire ropes, and other products. Their plasticity, strength, and uniformity of structure are crucial for subsequent processing. The delivery status of high carbon steel wire rods is generally hot-rolled, and its microstructure is a sorbite structure. Sorbite is a non-equilibrium pearlite-type structure and a dualphase mixed structure of sorbite ferrite and cementite. Its difference from pearlite is that a slice of sorbite and cementite in sorbite are thinner and the spacing between slices is smaller. Sorbite and cementite slices can be distinguished by a magnification of more than 600 times under an optical microscope. Due to the small slice spacing and many interphase boundaries, the sorbite structure has enhanced resistance to plastic deformation under the action of an external force, excellent strength, and plasticity, and is the most ideal structure for high-carbon wire rod steel [1]. In addition to sorbite, there are also pearlite, martensite, net cementite, and other structures in the high carbon steel wire rods. The content of sorbite is one of the important indicators to evaluate the performance of high-carbon wire rods. Generally, the content is required to be not less than 80%. The higher, the better.

At present, the standard for sorbite structure testing of high-carbon steel wire rods is the YB/T 169-2014 Metallegrephic Tat Method of Sorbite in High Carbon Steel Wire Rod [2]. Three detection methods are specified in the standard. The first method is the metallographic manual method. According to the stereological quantitative metallography principle, the grid number point method or grid truncation method is adopted, i.e., in a grid with a determined size or area, the sorbite volume fraction is calculated by manually counting the proportion of the grid intersection or grid length occupied by the sorbite structure to the total number of grid intersections or lengths. The second method is standard

**Citation:** Zhu, X.; Qian, L.; Yao, Q.; Huang, G.; Xu, F.; Chen, X.; Yao, Z. Automatic Detection of Sorbite Content in High Carbon Steel Wire Rod. *Metals* **2023**, *13*, 990. https:// doi.org/10.3390/met13050990

Academic Editors: Marcello Cabibbo and Andrea Di Schino

Received: 13 February 2023 Revised: 30 March 2023 Accepted: 24 April 2023 Published: 20 May 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

sample detection with an image analyzer. The standard sample with a determined sorbite content is used for synchronous sample preparation, corrosion, and detection with the sample to be tested to determine the reference grayscale value Gs of the standard reference sample. The sample to be tested is grayed with the same grayscale value Gs, and the sorbite volume fraction is quantitatively measured by the grayscale value. The third method is comparison. The microstructure of the sample to be tested through a metallographic microscope is observed, it is then compared with the standard rating chart of sorbite content, the standard chart with the closest structure to the sample to be tested is determined, and the sorbite volume fraction or sorbite grade of the sample to be tested is estimated. The three detection methods have their own advantages and disadvantages. Methods 1 and 2 produce more accurate detection results but have many detection steps and low efficiency. Method 3 is simple, fast, and efficient, but the error rate is large (the sorbite content of 10% is a grade in the standard chromatogram) and is greatly affected by the inspector. The consistency of the results is low.

At present, the detection of sorbite content is controversial, there are three reasons for this. Firstly, there is a heavy reliance on the individual researcher's background and experience which both introduce significant bias and potentially error into the process of detecting sorbite content. Secondly, the detection of sorbite content is greatly affected by the level of sample preparation; it is difficult to obtain a perfect microstructure to clearly distinguish the structure details and to determine the size of the region of a certain type of microstructure, due to the poor sample prepared by the polishing process. Corrosion defects can be caused by a number of factors, such as inadequate etching or over etching. Inadequate etching would lead to an incomplete microstructure display, and sorbite is easily misjudged as pearlite. Over etching would lead to the microstructure image showing a sense of relief, forming microstructure artifacts. Thirdly, there is a pearlite-sorbite transitional microstructure (PS) in high-carbon wire rods [3], and its lamellar spacing is between the pearlite and sorbite in high resolution; whether this type of transitional structure is classified as pearlite or sorbite also directly affects the judgment of the results. Therefore, the accurate detection of the sorbite content of high-carbon wire rods has been a problem in the steel industry.

With the rapid development of electronic information technology and artificial intelligence technology, computer vision (CV)—a discipline that studies how to make computers "see" like humans—is widely used in various industries, such as text recognition, and medical diagnosis [4,5]. The computers are objective and fast, and they are supposed to play an important role in the field of detection. In the field of materials, the automatic metallographic analysis of metal materials based on computer vision and machine learning technologies has become the focus of researchers at home and abroad. Chowdhury [6] used pre-trained convolutional neural networks (deep learning algorithms) to extract microstructure features, and used support vector machine, voting, nearest neighbors, and random forest models for classification, and obtained a high classification accuracy. Azimi [7] employed pixel-wise segmentation based on full convolutional neural networks (FCNN), together with the deep learning method to classify and identify the microstructure of mild steel, achieving a 93.94% classification accuracy. Park [8] employed a pixel-wise segmentation method via U-NET architecture built upon FCNN, and employed several techniques ranging from data augmentation, the Amazon computing service, to semantic segmentation. The system achieved a maximum classification accuracy of 98.689% for the pearlite and ferrite phases. Therefore, the automatic detection of the sorbite content based on machine learning is feasible. This paper builds a semantic segmentation model based on the deep learning algorithm and uses the sorbite tissue image calibrated by technical experts as the training data set to segment and analyze the sorbite, thus realizing the automatic detection of the sorbite content of high-carbon steel wire rods.

## **2. Acquisition and Calibration of Data Set**
