Indentation Image Analysis for Vickers Hardness Testing

Domínguez-Nicolás, Saúl M.; Wiederhold, Petra

doi:10.1109/iceee.2018.8533881

Cited by 12 publications

(29 citation statements)

References 6 publications

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“…The Vickers hardness number (VHN) was calculated after measuring the two diagonal d1,d2 length of the square impression which was formed by the indenter using scaled microscope (70X lens) in the device (13,14) and then converted to VHN by using the equation below:…”

Section: Microhardness Testmentioning

confidence: 99%

Evaluation of the Effect of Casein Phosphopeptide–Amorphous Calcium Phosphate Mousse versus Natural Raw Fresh Milk on Enamel Hardness After a pH Challenge

Al-Ani¹,

Al-Naimi²

2021

RDENTJ

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Aims:The aim of this study was to evaluate the effect of CPP−ACP, raw fresh buffalo milk and cow milk on the microhardness of artificial initial caries lesions. Materials and Methods: 100 sound maxillary first premolars were collected and randomly divided into four groups, the teeth in all groups were subjected to pH cycling procedure then treated with: Group1: n (25) deionized water, group2: n (25) CPP−ACP tooth mousse, group3: n (25) fresh raw cow milk, group4: n (25) fresh raw buffalo milk. Enamel surface was assessed by Vickers microhardness test device at a baseline and after demineralization and after remineralization. Results: In all groups, there were high statistically significant reduction of surface microhardness after demineralization. There was high statistically significant increase in surface microhardness in all groups except deionized water after remineralization, the highest remineralization effect was found in CPP−ACP tooth mousse group followed by the milk groups that showed encouraging results with fresh raw buffalo milk having superior results then fresh raw cow milk. Conclusions: CPP−ACP tooth mousse, raw fresh buffalo milk and cow milk were effective remineralizing agents which were reflected by increasing the surface microhardness of artificial initial caries lesion, but with different abilities, CPP−ACP tooth mousse was the best followed by buffalo milk then cow milk.

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Section: Microhardness Testmentioning

confidence: 99%

Evaluation of the Effect of Casein Phosphopeptide–Amorphous Calcium Phosphate Mousse versus Natural Raw Fresh Milk on Enamel Hardness After a pH Challenge

Al-Ani¹,

Al-Naimi²

2021

RDENTJ

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“…Our algorithm uses different binarization techniques with respect to those reported by other researchers [9,12,17,20], in that they do not use extreme and average gray values as binarization criteria. In addition, other algorithms [21] have employed similar binarization criterion to that employed in our algorithm, but without automatically determining the optimal binarization threshold from each input indentation image. Thus, the algorithm reported in [21] can semi-automatically determine the binarization criterion, but presents many outlier images in the vertices indentation detection.…”

Section: Limitations Of the Algorithms For Indentation Hardness Testingmentioning

confidence: 99%

“…Nevertheless, the light emitted by the durometer microscope causes illumination variations during image acquisition, which affect the performance of these algorithms. Recently, Domínguez-Nicolás et al [21] reported an algorithm for the analysis of Vickers hardness testing images, in which the binarization applied to the indentation image is based on maximum and average gray values. In addition, they employed an algorithm to detect corners, rather than edges, so as to locate the indentation vertices.…”

Section: Introductionmentioning

confidence: 99%

“…Our algorithm can evaluate a wide range of indentation features, where the edges of the indentation are not exactly straight lines, and where the gray values in both the indentation and background of the image are not uniform. This algorithm can measure the Vickers indentation hardness of specimens with indentation images with very low contrast in relation to the adjacent image area, in comparison with the limited capabilities of other algorithms [13][14][15][16][17]21] to detect indentation vertices. We used a Mitutoyo model HM-125 microdurometer with a diamond tip [22] to generate the indentation images that were subsequently tested using the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Algorithm for automatic detection and measurement of Vickers indentation hardness using image processing

Domínguez-Nicolás

Herrera-May

García-González

et al. 2020

Meas. Sci. Technol.

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In this paper, we present a novel algorithm for the automatic detection and measurement of Vickers indentation hardness, using image processing. This algorithm uses image segmentation via binarization, automatically evaluating the mean and extreme gray values by means of standard histogram equalization so as to determine the optimal binarization threshold from each input image. We use a morphological filter and region growing to identify the indentation footprint. Our algorithm determines the four indentation vertices required to calculate diagonal lengths and Vickers hardness number. This algorithm is applied to 230 indentation images of steel-316 and hafnium nitride specimens, obtained using a micro hardness machine. The proposed algorithm can measure the Vickers hardness number of specimens using their indentation images. The algorithm results have a relative error of less than 3% with respect to those obtained through a conventional manual procedure. This algorithm can be used for indentation images with low contrast and irregular indentation edges.

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“…Dominguez and Wiederhold implemented an algorithm in which the image is binarized based on its mean value, regardless of the shape of the histogram and determines the vertices of the slit, using the Harris–Stephen corner detection method. The lengths of the diagonals are then calculated to determine the Vickers hardness number, obtaining a maximum error of

[ 18 ]. Fedotkin et al proposed a method based on the comparison of the optical properties inside and outside the indentation, for which a series of circles are generated that allow information to be obtained from the histogram and the Hue distributions inside and outside them.…”

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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Indentation Image Analysis for Vickers Hardness Testing

Cited by 12 publications

References 6 publications

Evaluation of the Effect of Casein Phosphopeptide–Amorphous Calcium Phosphate Mousse versus Natural Raw Fresh Milk on Enamel Hardness After a pH Challenge

Evaluation of the Effect of Casein Phosphopeptide–Amorphous Calcium Phosphate Mousse versus Natural Raw Fresh Milk on Enamel Hardness After a pH Challenge

Algorithm for automatic detection and measurement of Vickers indentation hardness using image processing

Automatic Method for Vickers Hardness Estimation by Image Processing

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