Automated measurement of Vickers hardness using image segmentation with neural networks

Li, Zexian; Yin, Feng

doi:10.1016/j.measurement.2021.110200

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…Li and Yin implemented a CNN to predict a pixel mask and segment the area of the indentation impression from the background of the image. They then located a bounding box to measure the length of the diagonals and determine the hardness value on different materials such as titanium oxide, copper, and nylon, obtaining maximum relative errors for diagonal length between 0.33% and 1.67% [18]. Chen et al studied different CNN architectures (AlexNet, VGG, ResNet, GoogLeNet, and SqueezeNet) to evaluate hardness in chromium-molybdenum steel alloys (SCM 440) with revealed microstructure, finding that VGG16 yielded the lowest mean absolute error (MAE) of 10.2 [19].…”

Section: Introductionmentioning

confidence: 99%

Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks

Diaz

Ortega-Portilla

Mambuscay

et al. 2023

Metals

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The study of material hardness is crucial for determining its quality, potential failures, and appropriate applications, as well as minimizing losses incurred during the production process. To achieve this, certain criteria must be met to ensure high quality. This process is typically performed manually or using techniques based on analyzing indentation image patterns produced through the Vickers hardness technique. However, these techniques require that the indentation pattern is not aligned with the image edges. Therefore, this paper presents a technique based on convolutional neural networks (CNNs), specifically, a YOLO v3 network connected to a Dense Darknet-53 network. This technique enables the detection of indentation corner positions, measurement of diagonals, and calculation of the Vickers hardness value of D2 steel treated thermally and coated with Titanium Niobium Nitride (TiNbN), regardless of their position within the image. By implementing this architecture, an accuracy of 92% was achieved in accurately detecting the corner positions, with an average execution time of 6 seconds. The developed technique utilizes the network to detect the regions containing the corners and subsequently accurately determines the pixel coordinates of these corners, achieving an approximate relative percentage error between 0.17% to 5.98% in the hardness results.

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Section: Introductionmentioning

confidence: 99%

Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks

Diaz

Ortega-Portilla

Mambuscay

et al. 2023

Metals

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“…Jalilian and Uhl implemented deep learning techniques from a fully convolutional neural network (FCN) to locate, segment the indentation trace, determine the position and value of the diagonals, and subsequently obtain the hardness value, presenting high robustness to the size, location, and rotation of the indentation print, as well as the brightness and surface defects contained in the image [ 22 ]. Li and Yin implemented CNN to segment the indentation footprint of the image background, and, in turn, use a bounding box to measure the length of the diagonals and determine the value of hardness in different materials, showing maximum relative errors for the diagonals length of

for TiO 2 ,

Cu and

Nylon [ 23 ]. Cheng et al used indentation images of medium carbon chrome-molybdenum steel alloys (SCM 440) with revealed microstructure, making it difficult to see the indentation trace.…”

Section: Introductionmentioning

confidence: 99%

Automatic Method for Vickers Hardness Estimation by Image Processing

et al. 2022

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Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the surface of the material is measured after applying force with an indenter. The hardness is measured from the sample image, which is a tedious, time-consuming, and prone to human error procedure. Therefore, in this work, a new automatic method based on image processing techniques is proposed, allowing for obtaining results quickly and more accurately even with high irregularities in the indentation mark. For the development and validation of the method, a set of microscopy images of samples indented with applied forces of 5N and 10N on AISI D2 steel with and without quenching, tempering heat treatment and samples coated with titanium niobium nitride (TiNbN) was used. The proposed method was implemented as a plugin of the ImageJ program, allowing for obtaining reproducible Vickers hardness results in an average time of 2.05 seconds with an accuracy of 98.3% and a maximum error of 4.5% with respect to the values obtained manually, used as a golden standard.

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“…In addition, our method is much less affected by the surface roughness of the material. (9) In this study, the system flow chart and the structure of the CNN were first designed. Then, a dataset of Brinell indentation images containing manually labeled indentation regions was constructed, which was used to train a CNN.…”

Section: Introductionmentioning

confidence: 99%

Automatic Measurement Algorithm for Brinell Indentations Based on Convolutional Neural Network

Chen¹,

Fang²,

Tian³

et al. 2022

Sensors and Materials

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To avoid the interference of a material's surface factors in Brinell indentation images, which adversely affect measurement accuracy, an automatic measurement algorithm for Brinell indentations based on a convolutional neural network (CNN) is proposed. To eliminate the influence of factors such as scratches and collapses of the material surface on the measurement accuracy, the Brinell indentation image as the foreground is divided by the proposed algorithm and an indentation bounding box calculation is carried out after obtaining the binarized pixel mask of the indentation area. The measurement accuracy of the Brinell indentation image under the interference of some material background factors is thus improved. Our experimental results show that compared with the traditional automatic measurement method for Brinell indentations, Brinell indentation images with a complicated background environment can be measured more accurately by the proposed method, with the maximum relative error reduced by 20%. Moreover, the proposed method has strong applicability and high robustness for different material surfaces under different illumination conditions.

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Automated measurement of Vickers hardness using image segmentation with neural networks

Cited by 10 publications

References 14 publications

Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks

Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks

Automatic Method for Vickers Hardness Estimation by Image Processing

Automatic Measurement Algorithm for Brinell Indentations Based on Convolutional Neural Network

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