Measurement method of Brinell hardness
Brinell hardness measurement method hardness is the basic index reflecting the mechanical properties of materials, and Brinell hardness measurement method is one of the most widely used detection methods in metal hardness detection because of its large indentation and accurate measurement results. At present, the majority of the domestic use of optical microscope manual reading method for indentation measurement, and then look up the table or calculate the Brinell hardness measurement value. With the development of image sensor technology and image measurement technology, especially the rapid development of machine vision recognition technology in recent years, the method of brinell hardness indentation measurement using image measurement technology is rising rapidly. In addition to the direct measurement method of indentation, in recent years, sounding method has also appeared at home and abroad, which is to calculate the Brinell hardness by measuring the depth of the indenter pressed into the measured material and converting the area. In order to better understand the advantages and disadvantages of various methods, song Yue, Bai Lijuan, sun Xiaoran and other researchers from the physical and chemical testing center of general institute of steel research of hegang group analyzed the Brinell hardness measurement methods currently used at home and abroad, in order to improve the measurement methods.
Manual measurement method
Brinell hardness is that a tungsten carbide alloy ball with diameter D is pressed into the surface of the sample with test force F, and then the test force is removed after a specified time. The diameter D of the indentation on the surface of the sample is measured. Brinell hardness is proportional to the quotient of the test force divided by the surface area of the indentation. The indentation diameter is measured by optical reading microscope in the two mutually perpendicular directions of the indentation. The average diameter of the indentation is obtained according to the measured two indentation diameters, and then the Brinell hardness is calculated by the formula.
In the process of using optical reading microscope to measure indentation, the reading is sensitive to the light intensity, so this method requires a high light source. At the same time, the optical microscope placed on the surface of the sample is very easy to deflect, so the tester needs to be extra careful and repeatedly aim at the indentation boundary line, which causes great inconvenience to the indentation measurement and reduces the accuracy.
In order to solve this problem, Gao Fei et al. Studied an indentation measuring device for metal Brinell hardness tester. The device consists of magnetic switch part and micro fixing part. The sample is fixed by magnetic force, so that the sample is not easy to move in the measurement process. It is easy to operate and has strong applicability, so it is convenient to measure samples of different sizes.
When using the optical reading microscope to measure the indentation, the selection of the inner diameter and outer diameter of the indentation also has a great influence on the results. Zou Shengwen et al. Measured the outer diameter and inner diameter of the indentation, and obtained that the diameter 1 / 3 away from the outer diameter can more accurately and reliably represent the real size of the indentation.
In addition to optical microscope measurement, some improvements have been made on the basis of optical measurement. The indentation image is captured by charge coupled device (CCD) and input into computer. In the computer, three contour points of the indentation circle are selected along the boundary points of the indentation image to generate a fitting circle, and the diameter of the indentation circle is measured by manual wire drawing. Although this method is improved compared with the traditional method, it still needs human participation, which will bring large human error and low efficiency.
In recent years, the sounding method has also appeared at home and abroad, which can calculate the Brinell hardness by measuring the depth of the indenter pressed into the measured material and converting it into area, or directly compare the measured depth with the empirical database by fitting the empirical data with software.
Li Heping proposed that if the indentation diameter and Brinell hardness can be calculated according to the indentation depth, these shortcomings can be overcome, and the efficiency of automatic Brinell hardness measurement system and the comparability of measurement results can be improved. It points out that there is no distinction between “indentation depth” and “displacement of pressure bar with indenter” in many current standards and articles on hardness measurement methods, and puts forward a method to calculate indentation diameter and Brinell hardness by deducting indentation depth obtained from elastic deformation of hardness tester from maximum displacement of pressure bar, This method has the advantages of both Brinell hardness measurement and fast Brinell hardness measurement, and can obtain the measurement results of Brinell hardness accurately and quickly. Li Heping boldly predicted that the future Brinell hardness standard should include two methods, one is the traditional method to measure the indentation diameter, the other is the method to calculate the indentation depth by accurately measuring the displacement of the pressure bar, and then calculate the Brinell hardness.
Although sounding has verified its feasibility in a small range, it has not been widely verified, and with the aging of the equipment, its stiffness coefficient will have great uncertainty.
Image processing method based on image processing technology
Hardness measurement method based on image processing technology is a new technology with the development of computer technology, and it is also a key project actively invested in research and development at home and abroad. Using computer image processing technology to automatically measure the diameter of indentation circle, and automatically calculate the Brinell hardness of the tested sample, according to the principle of Brinell hardness measurement, the measurement accuracy and test efficiency are greatly improved.
Li Yongqian and others provided a method and device for measuring indentation diameter of hardness block. Firstly, the indentation image was taken, and then the image processing was carried out, including graying, histogram equalization, image enhancement, image filtering, contour detection, denoising, contour filling, contour extraction. Finally, the least square method was used to extract the indentation fitting diameter to obtain Brinell hardness.
Shan Zhongde et al. Used the automatic Brinell hardness measurement system based on machine vision to collect the indentation image, studied the algorithms of indentation image filtering, indentation image contour diameter extraction and diameter calibration coefficient, proposed an algorithm of indentation contour extraction based on particle swarm dynamic contour model (snake model), and introduced the indentation diameter calibration coefficient, The conversion relationship between indentation diameter pixels and indentation physical diameter in visual measurement is solved, and the calibration coefficient of diameter is fitted by least square method, which improves the measurement accuracy.
A variety of automatic indentation detection systems based on image processing technology are integrated. The main process is to use the camera to collect the image, and then carry out image denoising, edge detection and least square fitting to get the indentation diameter. Finally, the Brinell hardness is calculated according to the input coefficient. Most of the measurements in the literature are carried out under the condition that the image contour is very clear. The performance of the algorithm is not satisfactory for the image with poor lighting conditions, and the accuracy and robustness of the algorithm also need to be considered. In the future, with the wide application of high-resolution and high-precision image acquisition system, it may bring new vitality to the automatic indentation detection system.
A method based on deep learning
Although the Brinell hardness indentation measurement system based on image processing technology greatly improves the accuracy and efficiency of measurement, this method still has limitations. In the actual measurement, it is not always the standard sample with good surface treatment. In the measurement process, the surface condition, hardness grade and even roughness will have a great impact on the measurement results. Sometimes it is difficult to detect the indentation edge with image processing method. In this case, the method based on image processing technology may not meet the requirements of the test.
With the development of machine vision, deep learning has made great progress in image recognition. The image recognition method based on deep learning does not need complicated image processing as the traditional image recognition method does. This method is not limited by surface conditions and other factors. As long as the original image is input into the network and trained by a certain training set, the network can automatically extract the target and recognize the required features.
Tanakay et al. Used convolution neural network (CNN) to measure indentation diameter, which realized robust automatic measurement of Brinell hardness. On the basis of alexnet, a convolutional neural network system with five convolution layers and two fully connected layers is proposed to detect the indentation edge, and the network is trained by using the data set combined with the indentation edge image and the edge position given by manual measurement. The results show that the difference of indentation diameter between manual measurement method and CNN method is within 0.3%, which proves that this method is universal in the independence of hardness and surface characteristics of samples, and the measurement results are in good agreement with manual measurement results.
This method has strict requirements on data sets. Firstly, there must be enough data set images, and manual identification is also a time-consuming and laborious work. For the future, if the data sets measured by experts can be combined and shared publicly, it will be conducive to the development of training network.
Four existing methods of Brinell hardness measurement are summarized, including manual measurement method, depth measurement method, measurement method based on image processing technology and measurement method based on deep learning. This paper expounds the advantages and disadvantages of various methods. Although the manual measurement method is considered to be a reliable measurement method, it needs manual operation, and has human error and low efficiency. Although the sounding method has been proved to be feasible, it has not been widely verified. The automatic measurement system based on image processing technology shows good advantages in the accuracy and efficiency of measurement results, but it still has limitations for the samples with rough surface and no obvious indentation. With the development of computer technology, image recognition technology based on deep learning will make outstanding contributions to various fields. This method may become the focus of future research on Brinell hardness automatic measurement system.
Authors: Song Yue, Bai Lijuan, sun Xiaoran, Zhao Zhongyu, Xing Chengliang
Source: China Flanges Manufacturer – Yaang Pipe Industry (www.steeljrv.com)
(Yaang Pipe Industry is a leading manufacturer and supplier of nickel alloy and stainless steel products, including Super Duplex Stainless Steel Flanges, Stainless Steel Flanges, Stainless Steel Pipe Fittings, Stainless Steel Pipe. Yaang products are widely used in Shipbuilding, Nuclear power, Marine engineering, Petroleum, Chemical, Mining, Sewage treatment, Natural gas and Pressure vessels and other industries.)
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