The influence of sintering temperature on the mechanical evolution of Al2TiO5 flexible ceramics based on the acoustic emission

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

doi:10.1016/j.jallcom.2021.163004

Cited by 12 publications

(7 citation statements)

References 36 publications

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“…The mean energy value of cluster‐3 in AT‐13 is even greater than that of AT‐15. In my previous study, [ 25 ] the mean size (width and length) of AT grain in AT‐15 is about 4 times larger than that of AT‐13. The grains fracture energy of former should be 10 times more than that of latter.…”

Section: Resultsmentioning

confidence: 95%

“…The main reason is that the large grains are often accompanied by high‐sintering temperature. In this situation, the decomposition of AT grain is serious [ 25 ] (Figure 2), which makes the AT grains easier to fracture under the same stress. In addition, it is difficult for cracks to bypass large grains in the process of crack propagation.…”

Section: Resultsmentioning

confidence: 99%

“…There are more high‐level energy events related to cluster‐2 and cluster‐3 in initial stage (Figure 10a,b). In our previous study, [ 25 ] there are some big particles composed of some small grains in the AT‐13. These big particles are easier to fracture even under low stress.…”

Section: Resultsmentioning

confidence: 99%

“…The characteristics of raw materials and main preparation process of material can consult the ref. [25]. It needed to be pointed out that the specimens with sintering temperature of 1300, 1400, 1500, and 1600 °C are marked as AT‐13, AT‐14, AT‐15, and AT‐16, respectively.…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2023

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“…As such, AT-FC has attracted more and more researcher's attention. [1][2][3][4][5] Since the strength of grain boundary microcracks is weak, the AT grains are easy to move even under low stress, result-ing in bad mechanical properties of AT-FC. If the grain boundary microcracks are strengthened, the mechanical properties should be improved.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in Al₂TiO₅ flexible ceramics with microcrcaks strengthened for damage mode automatic identification

Shan

Qian

Xue

et al. 2023

Int J Applied Ceramic Tech

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The damage modes of Al2TiO5 flexible ceramics (AT) with microcracks strengthened were studied by machine leaning for damage mode identification. Results show that the optimal number of clusters increases from 3 to 4 with the increase of ZrO2 content. Meanwhile, the concentrated region of cluster with the same label gradually shifts to the direction of low amplitude with increase of ZrO2. In addition, the quantity of cluster is easy saturated since the AT grain boundary microcracks are strengthened by the formation of ZrTiO4. For achieving the automatic identification of different damage modes, support vector machine based on Kennard‐stone training set selection was used to distinguish the damage modes of other samples in the same sample set. Almost the same evolution trend of corresponding clusters in new samples and training samples proves high efficiencies of this method.

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Sintering Temperature Dependence of the Flexibility of ZrO₂‐Modified Aluminum Titanate Flexible Ceramics

Shui

Shan

et al. 2023

Adv Eng Mater

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ZrO2‐modified aluminum titanate (Al2TiO5, AT) flexible ceramics are prepared by solid‐state reaction method. The influence of sintering temperature and holding time on the microstructure and mechanical properties is investigated. Temperature‐dependence behavior of the flexibility is observed. It is believed that temperature‐dependence behavior of AT grain aspect ratio should take responsibility. However, average value of the aspect ratio remains unchanged regardless of holding time. Reasons are as follows: the formed zirconium titanate (ZrTiO4, ZT) grains could pin in AT grain boundaries, and internal stress generates between AT/ZT boundaries during grain growth process, thus inhibiting AT grain growth and reducing its growth rate, leading to AT grain refinement and increase of its aspect ratio. But, the increase of holding time reduces the inhibition effect and the increase of temperature strengthens the effect, resulting in rapid AT grain growth, especially its width. As a result, though the length and width of AT grains increase with the increase of holding time, its aspect ratio remains unchanged. On these bases, AT grain growth model related to holding time is proposed. It is expectable that the discovery of this study can deepen the research of particle pinning and provide a reference for abnormal grain growth.

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The influence of sintering temperature on the mechanical evolution of Al2TiO5 flexible ceramics based on the acoustic emission

Cited by 12 publications

References 36 publications

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

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

Machine learning in Al₂TiO₅ flexible ceramics with microcrcaks strengthened for damage mode automatic identification

Sintering Temperature Dependence of the Flexibility of ZrO₂‐Modified Aluminum Titanate Flexible Ceramics

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The influence of sintering temperature on the mechanical evolution of Al2TiO5 flexible ceramics based on the acoustic emission

Cited by 12 publications

References 36 publications

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

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

Machine learning in Al2TiO5 flexible ceramics with microcrcaks strengthened for damage mode automatic identification

Sintering Temperature Dependence of the Flexibility of ZrO2‐Modified Aluminum Titanate Flexible Ceramics

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Product

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

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

Machine learning in Al₂TiO₅ flexible ceramics with microcrcaks strengthened for damage mode automatic identification

Sintering Temperature Dependence of the Flexibility of ZrO₂‐Modified Aluminum Titanate Flexible Ceramics