Limit of detection and hardness evaluation of some steel alloys utilizing optical emission spectroscopic techniques

ElFaham, Mohamed M.; Okil, M.; Mostafa, Ayman M.

doi:10.1016/j.optlastec.2018.07.022

Cited by 40 publications

(13 citation statements)

References 24 publications

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“…In some works, the parameters obtained using LIBS have been related to the hardness of the material. 17–19 In particular, a linear relationship between the plasma temperature and Vickers hardness has been found on steel by LIBS tests. 18 In that work, different steel compositions were used.…”

Section: Resultsmentioning

confidence: 89%

“…17–19 In particular, a linear relationship between the plasma temperature and Vickers hardness has been found on steel by LIBS tests. 18 In that work, different steel compositions were used. On the other hand, in the present work, the samples would exhibit different values of hardness in relation to the fraction of copper particles.…”

Section: Resultsmentioning

confidence: 89%

“…LOD is a parameter that is generally reported and this is defined as the lowest concentration of an analyte that can be detected with a predetermined confidence level. 18 In LIBS measurements, LOD is calculated from the slope of the linear regression (Fig. 3) and the standard deviation of the background σ as follows 18 …”

Section: Resultsmentioning

confidence: 99%

“…Other mechanical parameters could also be considered, such as the elastic modulus, which also presents an increase with the filler content, but differs slightly from a linear behavior. On the other hand, although several authors can use a linear function between plasma temperature and hardness, in steel 18 or graphite rubber composites, 17 for example, the values of plasma temperature are different between varied materials. However, it is worth mentioning that measuring the hardness in composite materials with inclusions of tens of microns in diameter is not easy, because the imprint of the usual techniques, such as Vickers, has dimensions of the same order.…”

Section: Resultsmentioning

confidence: 99%

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Potentialities of a Laser-Induced Breakdown Spectroscopy Technique in the Study of Polymer Composites

et al. 2020

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A laser-induced breakdown spectroscopy (LIBS) technique was used to evaluate the filler content in particulate epoxy–copper composites. A potential application for a direct and fast measurement of the filler in composites through the LIBS results is suggested using calibrated samples. The methodology used in this work makes possible the incorporation of LIBS as a quantitative technique for the study of particle metal-filled epoxy composites, providing a method to obtain a direct estimation of the filler volume fraction.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Potentialities of a Laser-Induced Breakdown Spectroscopy Technique in the Study of Polymer Composites

et al. 2020

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“…Therefore, LIBS has developed rapidly in the past two decades. The elements can be detected whether sample is solid [1][2][3], liquid [4,5] or gas [6]. LIBS has been applied to various fields: environment [7][8][9], biomedical [10][11][12], industry [13][14][15][16], and agriculture [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

Yang

Hao

Zhang

et al. 2020

Sensors

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Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores’ selection in the metallurgical industry.

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A comparative study on the color change of pigments due to the consolidation of conventional spectroscopic techniques and laser-induced breakdown spectroscopy

et al. 2019

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Limit of detection and hardness evaluation of some steel alloys utilizing optical emission spectroscopic techniques

Cited by 40 publications

References 24 publications

Potentialities of a Laser-Induced Breakdown Spectroscopy Technique in the Study of Polymer Composites

Potentialities of a Laser-Induced Breakdown Spectroscopy Technique in the Study of Polymer Composites

Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

A comparative study on the color change of pigments due to the consolidation of conventional spectroscopic techniques and laser-induced breakdown spectroscopy

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