Modeling the temperature dependent ultimate tensile strength for unidirectional ceramic-fiber reinforced ceramic composites considering the load carrying capacity of broken fibers

Zhang, Xin; Li, Weiguo; Deng, Yong; Liu, Ying; Zheng, Shifeng; Dong, Pan; Wang, Shubin; Zhang, Xi; Shen, Zheng; Ma, Jianzuo

doi:10.1016/j.ceramint.2019.08.145

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…According to an analytical model by Zhang et al [167], the volume fraction of broken fibers, Vfb (T), and the load carrying capacity, fb(T), at different temperatures, can be modeled as…”

Section: Physics-based Models Availablementioning

confidence: 99%

“…For detailed explanations of the terms, refer to [167]. Evan presented a model for predicting the composite ultimate strength based on weakest link statistics, incorporating the fiber Weibull modulus, m [168].…”

Section: Physics-based Models Availablementioning

confidence: 99%

“…Table 19 summarizes key material properties of CMCs and PMCs [165]. For additional data, refer to [167][168][169][170].…”

Section: Data Availablementioning

confidence: 99%

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Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest

Steingrimsson¹,

Fan²,

Kulkarni³

et al. 2021

High-Entropy Materials: Theory, Experiments, and Applications

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This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and composites with large composition spaces for structural materials. Such alloys or composites are referred to as high-entropy materials (HEMs) and are here presented primarily in context of structural applications. For each output property of interest, the corresponding driving (input) factors are identified. These input factors may include the material composition, heat treatment, manufacturing process, microstructure, temperature, strain rate, environment, or testing mode. The framework assumes the selection of an optimization technique suitable for the application at hand and the data available. Physics-based models are presented, such as for predicting the ultimate tensile strength (UTS) or fatigue resistance. We devise models capable of accounting for physics-based dependencies. We factor such dependencies into the models as a priori information. In case that an artificial neural network (ANN) is deemed suitable for the applications at hand, it is suggested to employ custom kernel functions consistent with the underlying physics, for the purpose of attaining tighter coupling, better prediction, and for extracting the most out of the -usually limited -input data available. 1 The chapter is dedicated to the loving memory of Dr. Steingrimur Baldursson, Professor Emeritus at the University of Iceland (1930Iceland ( -2020. In 1958, prior to returning back to Iceland to take on a position within the Faculty of Sciences at the University of Iceland, Dr. Baldursson completed a doctorate from the University of Chicago, under guidance of Prof. Joseph E. Mayer and Prof. Maria G. Mayer, a 1963 Nobel Laureate in Physics.

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Section: Physics-based Models Availablementioning

confidence: 99%

Section: Physics-based Models Availablementioning

confidence: 99%

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Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest

Steingrimsson¹,

Fan²,

Kulkarni³

et al. 2021

High-Entropy Materials: Theory, Experiments, and Applications

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“…Currently, most studies involved subjecting the fibers to elevated temperatures in different environments and then performing mechanical tests at room temperature. It has been demonstrated that high temperatures will lead to the transformation of fiber microstructure, which will have a negatively impact on their mechanical properties 15–17 . However, these methods merely yield results about the residual strength of the fibers after thermal loading and tensile tests are conducted following the heat treatment 18–20 .…”

Section: Introductionmentioning

confidence: 99%

“…It has been demonstrated that high temperatures will lead to the transformation of fiber microstructure, which will have a negatively impact on their mechanical properties. [15][16][17] However, these methods merely yield results about the residual strength of the fibers after thermal loading and tensile tests are conducted following the heat treatment. [18][19][20] Such approaches fail to reflect the actual behavior of ceramic fibers under the influence of thermal loads.…”

Section: Introductionmentioning

confidence: 99%

Study of online tensile properties for continuous ceramic fibers under high‐temperature loads

Zhang,

Jiang,

et al. 2024

Int J Applied Ceramic Tech

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In this study, a self‐developed online test platform for high‐temperature mechanical properties was utilized to test the tensile properties of three continuous ceramic fibers from room temperature to 1 000°C. It was observed that the tensile strength of all tested continuous ceramic fibers decreased significantly with increasing temperature. Specifically, significant peeling of the surface organic coating from room temperature to 400°C, inherent and newly formed defects exposed from 600°C to 1 000°C contributed to the decrease in tensile strength. More importantly, compared with room temperature, the strength of the silicon carbide fibers and the alumina fibers remained 29.7% and 20.2% respectively when stretched online at 1 000°C, while the quartz fibers dropped to 4.3% dramatically. These findings contribute valuable data for the potential application of ceramic fiber filaments as flexible thermal protection materials.

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Theoretical characterization of the temperature‐dependent ultimate tensile strength of short‐fiber‐reinforced polymer composites

Yang

Liu

et al. 2021

Polymer Composites

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Short fiber/polymer composites (SFPCs) are increasingly applied in the field of high-temperature structures. Its reliability at high temperatures has always been the core issue concerned by researchers. Here, utilizing the Force-Heat Equivalence Energy Density Principle, we developed a physically based model of temperature-dependent ultimate tensile strength (TDUTS) for SFPCs. This model quantitatively considers the evolution of residual thermal stress, interface, fiber, and matrix performance as temperature increases. Meanwhile, the influence of fiber agglomeration frequently observed in composites and fiber orientation and length distribution is taken into account in the proposed model. The model predictions over a wide temperature range are validated against the experimental data available from the literature, indicating its reasonability and accuracy. The comparison with Kelly-Tyson model and Li's model is also performed. Additionally, the quantitative effects of the fiber agglomeration and interfacial shear strength on the TDUTS are discussed in detail. The present research not only provides an accurate and feasible approach to estimate the TDUTS of SFPCs, but also offers some helpful insights for the fabrication and design of such composites.

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Modeling the temperature dependent ultimate tensile strength for unidirectional ceramic-fiber reinforced ceramic composites considering the load carrying capacity of broken fibers

Cited by 7 publications

References 49 publications

Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest

Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest

Study of online tensile properties for continuous ceramic fibers under high‐temperature loads

Theoretical characterization of the temperature‐dependent ultimate tensile strength of short‐fiber‐reinforced polymer composites

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