The tensile damage behavior of SiC<sub>f</sub>/SiC–B<sub>4</sub>C after oxidation in wet atmosphere based on acoustic emission pattern recognition

Shan, Qingliang; Xu, Qiankun; Xue, Yudong; Lian, Jiawei; Wang, Yewei; Chen, Changrong; Zhou, Yuqian; Qian, Ma; Shui, Anze

doi:10.1111/jace.17816

Cited by 9 publications

(21 citation statements)

References 32 publications

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“…The change of cumulative AE Hits (%) in risetime with the loading of composites after oxidation for 100 h (A) No-Al-100 h; (B)-(D) the clustering result of (A); (E) Al-100 h; (F)-(H) the clustering result of (E). AE, acoustic emissionIt needs to be pointed out that the proportions of Crack-4 in my previous study17 are almost the same with oxidation time, whereas the proportions of Crack-4 in this study show a downward trend with prolongation oxidation time. Generally, the longer the oxidation time, the more serious the oxidized part of composites.…”

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confidence: 45%

“…The specific steps are as follows (the basic principle of each step can be found in Refs. [17,18]): (1) the selection of uncorrelated six AE features, count (C), duration (D), risetime (R_T), energy (E), amplitude (A), and peak frequency (P_F), was done in this study. (2) The determination of cluster valid number: Before employing k-means to group, the clusters number of k should be determined in advance.…”

Section: Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

“…Moreover, these signal parameters should only represent one particular damage mechanism. 17,18 As the distributions of AE parameters will be overlapping, it is of significance to be precisely bounded each damage behavior of SiC f /SiC-B 4 C composites to AE signal parameters. If all the signals corresponded to each damage behavior can be accurately selected, the oxidation degree of each part of SiC f /SiC-B 4 C composites could be more comprehensively understood by comparing the changes of corresponding signals before and after oxidation.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, a single‐damage behavior, such as fibers/matrix debonding, can generate multiple AE key parameters. Moreover, these signal parameters should only represent one particular damage mechanism 17,18 . As the distributions of AE parameters will be overlapping, it is of significance to be precisely bounded each damage behavior of SiC f /SiC–B 4 C composites to AE signal parameters.…”

Section: Introductionmentioning

confidence: 99%

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The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

Shan

Xue

2022

J Am Ceram Soc.

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To study the anti‐oxidation mechanism of SiCf/SiC–B4C modified with Al2O3 in wet atmosphere, the damage evolution of composites after oxidation was explored by unsupervised machine learning technology (k‐means). Results display that the mean feature values of cluster‐1 (some small cracks in oxidation layer and matrix as well as fiber debonding) in composites modified with Al2O3 are larger than that in virgin after oxidation. Meanwhile, as the oxidation time increases, the concentrated area of cluster‐2 (fiber breakage, cracks in axial and transverse yarns) in both composites gradually shifts toward the direction of high mean feature values. Because Al2O3 can protect the BN from oxidation, the increase of peak frequency and risetime of composites modified with Al2O3 is less than that of virgin. Moreover, the proportion of cluster‐3 (big transverse matrix cracks) in composites modified with Al2O3 is always more than that of virgin. Based on the research results, the corresponding relationship between each damage behavior and each cluster is thoroughly built.

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confidence: 45%

Section: Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

Shan

Xue

2022

J Am Ceram Soc.

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“…Among these, the RMI process displays significant advantages of a short preparation time, simple process, low cost, and the ability to prepare complex materials with a low porosity. 9 − 11 Thus, it has great potential in the development of low-porosity SiC/SiC composites that can be applied in engineering.…”

Section: Introductionmentioning

confidence: 99%

Oxidation Behavior of the Si-B-X (X = Mo, Cr, or Ti) Alloys in the Temperature Range of 1000–1400 °C

Zeng

Xiong

et al. 2022

ACS Omega

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Low-melting-point silicon–boron system alloys are promising for low-temperature reactive melt infiltration to reduce high-temperature damage to silicon carbide fibers during the densification of SiC/SiC composites. Meanwhile, the oxidation resistance of the alloys will have a large impact on the intrinsic oxidation resistance of the composite. Herein, three alloys, Si-14.88B-7Mo, Si-14.88B-7Ti, and Si-14.88B-7Cr, were fabricated to investigate the oxidation behavior in air at 1000–1400 °C. The results showed that the oxidation weight gains of the Si-B-Mo alloy after oxidation at 1400 °C for 100 h were 0.9 mg/cm –2 , which were only 50 and 1.5% of those of Si-B-Ti and Si-B-Cr alloys, respectively. The excellent oxidation resistance of Si-B-Mo alloys at 1000–1400 °C was attributed to the formation of glassy-surface layers and the dense internal oxide layer. The dense oxide layer and the low solubility of Mo ions in SiO 2 inhibit the volatilization of MoO 3 and the oxidation reaction, reducing the oxidation rate of the Si-B-Mo alloy. The difference in the coefficients of thermal expansion for SiO 2 and TiO 2 led to penetrating cracks in the oxide layer of the Si-B-Ti alloy during cooling, thereby reducing the oxidation resistance. In addition, the rate of volatilization of Cr 2 O 3 as CrO 3 in an oxidation atmosphere above 1200 °C increased significantly in the Si-B-Cr alloy. The simultaneous volatilization of B 2 O 3 and CrO 3 resulted in the formation of loose oxide layers in the CrB 2 region of the Si-B-Cr alloy, leading to severe oxidation.

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The Damage Mode Automatic Identification of Al₂TiO₅ Flexible Ceramics Based on Machine Learning

et al. 2023

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Al 2 TiO 5 flexible ceramics (AT-FCs) are attractive material with nonlinear stress-strain behavior due to their multi-cracks structure. [1,2] The application of AT-FC to prepare shockproof component especially in high temperature has emerged as a new and viable choice. Although researches toward enhancing the study of AT-FC have been going on for more than 10 years, the damage-mode evolution inside still remains elusive. [3][4][5] Using nondestructive techniques will increase the confidence of both engineers and researchers in the full utilization of the potential of this material. Acoustic emission (AE) is widely used in real-time monitoring the damage process in materials. [6][7][8][9][10][11][12][13][14][15] Generally, the gathered AE data will be saved as the signal parameters such as energy, count, rise time, duration, and amplitude. These key parameters can be utilized directly or as a derived combinations. [11] In AT-FC, the possible AE single source mainly results from the fracture of grain boundary glass phase, friction between grains, and grains failure. [16] Meanwhile, a single-damage mode, such as fracture of grain boundary glass phase, can provide multiple AE signal parameters. In addition, the overlapping of AE parameters distribution area is ineluctable due to the equipment settings, dense AE from various signal sources, and signal fading. [11,17] Therefore, the relationship between each damage mode of AT-FC and AE signal parameters has not been built. However, to achieve damage-mode automatic identification of AT-FC, it is necessary to build this relationship in advance. Machine learning (ML) is a suitable method for accomplishing the corresponding relationship between each damage mode of AT-FC and AE signal parameters to realize automatic identification of damage mode. The ML algorithms contain two modes, unsupervised and supervised. [18] The target of unsupervised ML is to classify the AE parameters with similar characteristics into groups and utilize those groups to distinguish different damage modes, while supervised ML is used to identify the damage modes of other materials in the same set using the distinguished damage modes.Generally, the AE signals handled by ML algorithms have been concentrated on the mechanical properties analysis of fiber-reinforced ceramic/glass matrix composites. Moevus et al. [19] employed the unsupervised ML algorithms, k-means, to explore the damage modes of two kinds of SiC f /[Si-B-C] composites. Five and four damage modes were accurately distinguished in elongation composites and stiff composites, respectively. Meanwhile, Kostopoulos et al. [13] successfully identified three damage modes in SiC/mixed abrasive slurry-L (ceramic glass matrix) composites utilizing k-means algorithms. However, it is necessary to combine the unsupervised ML algorithms with supervised ML algorithms to achieve the automatic identification

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The tensile damage behavior of SiC_f/SiC–B₄C after oxidation in wet atmosphere based on acoustic emission pattern recognition

Cited by 9 publications

References 32 publications

The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

Oxidation Behavior of the Si-B-X (X = Mo, Cr, or Ti) Alloys in the Temperature Range of 1000–1400 °C

The Damage Mode Automatic Identification of Al₂TiO₅ Flexible Ceramics Based on Machine Learning

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The tensile damage behavior of SiCf/SiC–B4C after oxidation in wet atmosphere based on acoustic emission pattern recognition

Cited by 9 publications

References 32 publications

The anti‐oxidation mechanism of SiCf/SiC–B4C modified with Al2O3 in wet atmosphere based on machine learning

The anti‐oxidation mechanism of SiCf/SiC–B4C modified with Al2O3 in wet atmosphere based on machine learning

Oxidation Behavior of the Si-B-X (X = Mo, Cr, or Ti) Alloys in the Temperature Range of 1000–1400 °C

The Damage Mode Automatic Identification of Al2TiO5 Flexible Ceramics Based on Machine Learning

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The tensile damage behavior of SiC_f/SiC–B₄C after oxidation in wet atmosphere based on acoustic emission pattern recognition

The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

The Damage Mode Automatic Identification of Al₂TiO₅ Flexible Ceramics Based on Machine Learning