Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses

Deng, Yifan; Zeng, Huidan; Jiang, Yejia; Chen, Guorong; Chen, Jianding; Sun, Luyi

doi:10.1088/2053-1591/aab723

Cited by 10 publications

(9 citation statements)

References 33 publications

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“…The effects of the substrate were reduced by choosing smaller penetration depths. A comparison with the results in Oseli et al [ 17 ] showed agreement to the expected extent (about 3–12% difference). It should be noted that results of Oseli et al [ 17 ] were adapted to different fibers (E-glass fibers, basalt fibers, melted mineral wool fibers) and a fitted line for the combined content of SiO 2 + Al 2 O 3 as a function of modulus of elasticity was constructed.…”

Section: Resultssupporting

confidence: 84%

“…A comparison with the results in Oseli et al [ 17 ] showed agreement to the expected extent (about 3–12% difference). It should be noted that results of Oseli et al [ 17 ] were adapted to different fibers (E-glass fibers, basalt fibers, melted mineral wool fibers) and a fitted line for the combined content of SiO 2 + Al 2 O 3 as a function of modulus of elasticity was constructed. The values fitted to the chemical composition of the present study were used to compare the values with the results of modulus of elasticity in the present study.…”

Section: Resultssupporting

confidence: 84%

“…In sample S1, the proportion of SiO 2 and Al 2 O 3 was the highest, while sample S2 had the highest hardness, where a higher proportion of CaO was measured. Comparing the results with those of Deng et al [ 17 ], the sample with the lowest SiO 2 content, which also had the highest CaO content, should have the highest hardness. The Al 2 O 3 content was practically the same in samples S1, S2 and S4, while it was slightly lower in sample S3, which affected the measured hardness values.…”

Section: Resultssupporting

confidence: 58%

“…Zhao et al [ 14 ] investigated the influence of chemical composition on the tensile strength and corrosion resistance of rock, glass and slag wool fibers and showed a significant influence of melt composition. In addition to tensile strength, the modulus of elasticity [ 15 , 16 , 17 ] and hardness [ 16 , 17 , 18 ] of the fibers are also important properties that determine the mechanical properties of the finished mineral wool product. Due to the small fiber diameters (typically less than 10 µm), the measurement of these properties requires specialized measurement methods such as nanoindentation [ 19 , 20 ] or Sentmanat extensional rheometry [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Physical Properties of Mineral Fibers Depending on the Mineralogical Composition

et al. 2021

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A developed methodology for determining the physical properties of mineral fibers prepared from different input mixtures under the same spinning wheel conditions is described and discussed. Energy dispersive X-ray fluorescence spectroscopy was combined with simultaneous thermal analysis and thermogravimetry to study the mineralogical composition and typical melting and crystallization temperatures. The mechanical properties measured with nanoindentation were related to the mineralogical properties and the results obtained are in agreement with the literature. The developed methodology shows reliable performance and demonstrates the ability to study the mechanical properties of mineral fibers, their mineralogical composition, and thermal properties. The presented experimental methodology opens up the possibility of researching the mechanical properties of mineral fibers for the purpose of defining production recipes in the field of mineral thermal insulation materials.

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Section: Resultssupporting

confidence: 84%

Section: Resultssupporting

confidence: 84%

Section: Resultssupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

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Physical Properties of Mineral Fibers Depending on the Mineralogical Composition

et al. 2021

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“…With the help of MD simulation, we could calculate the constraint strength and bond angle distributions, which makes the modeling more powerful and precise. Besides MD simulation, machine learning offers a unique way to design glassy materials through quantitative structure‐property relationship (QSPR) . Combining machine learning with topological constraint theory is promising for future research.…”

Section: Conclusion Perspectives and Challengesmentioning

confidence: 99%

Progress in modeling of glass properties using topological constraint theory

Zheng

Zeng

2020

Int J of Appl Glass Sci

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Prediction of glass properties as a function of composition has been a long-standing problem. Topological constraint theory has built the quantitative relationships between composition and macroscopic properties, which can be used to predict compositional dependence of glass properties. In this review, we will summarize the recent important progress for the development of the theory. The topological model for glass transition temperature has been proposed from temperature-dependent constraint theory. We will also discuss the effect of water on topological constraint, how to account for mixed alkali effect and mixed alkaline earth effect, and the application of the model to new glass family besides inorganic glasses. Topological models for hardness and thermal expansion have also been developed. Topological models for predicting elasticity have been recently proposed. We also show some example on the application of the theory in designing new glasses and we suggest some future directions of the theory.

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Bioremediation of turquoise blue by Mangifera indica — particle swarm optimization and kinetic modeling

Deshannavar,

Sivaprakash,

Rajamohan

et al. 2023

Biomass Conv. Bioref.

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Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses

Cited by 10 publications

References 33 publications

Physical Properties of Mineral Fibers Depending on the Mineralogical Composition

Physical Properties of Mineral Fibers Depending on the Mineralogical Composition

Progress in modeling of glass properties using topological constraint theory

Bioremediation of turquoise blue by Mangifera indica — particle swarm optimization and kinetic modeling

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