Quantitative study on nonmetallic inclusion particles in steels by automatic image analysis with extreme values method

Barbosa, Cássio; Campos, José Brant de; Nascimento, J. L. do; Caminha, Ieda Maria Vieira

doi:10.1016/s1006-706x(09)60054-6

Cited by 6 publications

(1 citation statement)

References 6 publications

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“…Tukey's test complements the ANOVA by performing all pairwise mean comparisons to identify which mean differs from the other. Both have been jointly applied in materials' science as by (de Vasconcellos et al, 2006;Felipe et al, 2018;Lee et al, 2020;Moorehead et al, 2018;Rankouhi et al, 2016;), or even other test combinations (Barbosa et al, 2009;Roa et al, 2015;, but almost no works available in the literature mentions if the necessary validation to support their findings has been done.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of Contaminants on Microhardness Sintered Boron Carbide Samples

Aguilera

Filho

Neto

et al. 2022

JART

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This work presents ANOVA and Tukey’s statistic tests that were carried out in Knoop microhardness results of boron carbide sintered samples. Three samples, A, B and C, of different processing conditions were tested and statistical analysis showed that microhardness results are strongly influenced by porosity and chemical composition, mainly due to presence of C(2H) and WB2.5 contaminant phases. Those phases promoted higher densification in A and B samples and it was found that A ≈ B > C is the relation between the microhardness results of the samples, while A ≈ B < C is the porosity ratio of the samples. Furthermore, this work aims to apply a statistical tool to evaluate the microhardness results performed on advanced ceramics and, using these results, to control parameters in the sintering process, showing that a deeper statistical analysis is necessary for the interpretation and validation of the results merit.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of Contaminants on Microhardness Sintered Boron Carbide Samples

Aguilera

Filho

Neto

et al. 2022

JART

View full text Add to dashboard Cite

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A method for observing the three-dimensional morphologies of inclusions in steel

Bao

Wang

Jiang

2012

Int J Miner Metall Mater

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Automatic characterization of spherical metal powders by microscope image analysis: a parallel computing approach

Cui

Shonkwiler

et al. 2023

J. Iron Steel Res. Int.

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The microstructural characteristics of spherical metal powders play an important role in determining the quality of mechanical parts manufactured by powder metallurgy processes. Identifying the individual powder particles from their microscopic images is one of the most convenient and cost-efficient methods for the analysis of powder characteristics. Although numerous image processing algorithms have been developed for automating the powder particle identification process, they perform less accurately in identifying adjacent particles that are heavily overlapped in their image regions. We propose an automatic algorithm to robustly and accurately identify spherical powder particles, especially heavily overlapped particles, from their microscope images. A parallel computing framework is designed to further enhance the computational efficiency of the proposed algorithm. Powder characteristics such as particle size distribution and the location of potential satellite particles have been derived from our identification results. The accuracy and efficiency of our algorithm are validated by real-world scanning electron microscope images, outperforming other existing methods and achieving both precision and recall above 99%.

show abstract

Quantitative study on nonmetallic inclusion particles in steels by automatic image analysis with extreme values method

Cited by 6 publications

References 6 publications

Analysis of the Influence of Contaminants on Microhardness Sintered Boron Carbide Samples

Analysis of the Influence of Contaminants on Microhardness Sintered Boron Carbide Samples

A method for observing the three-dimensional morphologies of inclusions in steel

Automatic characterization of spherical metal powders by microscope image analysis: a parallel computing approach

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