Correlation between failure mechanism and rupture lifetime of 2D-C/SiC under stress oxidation condition based on acoustic emission pattern recognition

Lyu, Peng; Yao, Leijiang; Ma, Xiaofei; An, Gang; Bai, Guodong; Augousti, A.T.; Tong, Xiaoyan

doi:10.1016/j.jeurceramsoc.2020.06.070

Cited by 19 publications

(7 citation statements)

References 37 publications

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“…This polymer nanocomposite presents an opportunity for use in load‐bearing applications as it has demonstrated a substantial increase in mechanical strength. One area of proposed future work is to extend the use of acoustic signals for monitoring and analysis by applying more advanced acoustic detection and processing techniques used elsewhere, 58,59 in order to investigate potential failure mechanisms. It is anticipated that the insight gained by a greater understanding of these failure mechanisms can inform future production of nanocomposites with yet further improved mechanical properties.…”

Section: Resultsmentioning

confidence: 99%

Investigation of the elastic properties of poly (methyl methacrylate) reinforced with graphene nanoplatelets

Muhammettursun

Bel

Kocacinar

et al. 2021

J of Applied Polymer Sci

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The technique for synthesis of poly (methyl methacrylate) (PMMA) by atom transfer radical polymerization has been strengthened by using graphene nanoplatelets (GNPs) to enhance the elastic properties of the polymer. In order to improve practical, economical and mechanical performance, the requirements for effective implementation of production control as a smart bulk polymer nanocomposite were determined for cost-effective bulk production. Threedimensional inspection (using an ultrasound interrogation method for the whole volume under test) confirmed the synthesis of the nanocomposite to be free of agglomeration and bubbles. As a result of this elimination of defects, an enhancement in compressive strength of 42.7% was achieved and the Rockwell hardness was increased by 19.9% through the addition of GNPs at 2 wt% by mass. The deformation and mechanical failure properties have been characterized in the mechanical enhancement of the polymer nanocomposite. Elastic parameters determined using ultrasound testing identified that changes in the structural features following the addition of these GNPs were uniquely connected to the enhancements in these elastic parameters (such as Young's modulus, Poisson's ratio, shear modulus, and microhardness) of the PMMA/GNPs nanocomposite.

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Section: Resultsmentioning

confidence: 99%

Investigation of the elastic properties of poly (methyl methacrylate) reinforced with graphene nanoplatelets

Muhammettursun

Bel

Kocacinar

et al. 2021

J of Applied Polymer Sci

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“…Frequency depends on the properties of composition involved in the micro-fracture process. Thus, cluster labeling can be proposed according to the Ab-energy and peak frequency [13]. Figure 6 illustrates the energy and peak frequency of the cluster centers of all the three samples.…”

Section: Cluster Labelingmentioning

confidence: 99%

“…In recent years, researchers have conducted many investigations on its mechanical properties of C/SiC composites. They often rely on physical information released from the internal changes in materials to describe the damage process, such as electrical resistance, mechanical parameters, acoustic emission (AE) [11][12][13][14][15][16][17]. AE is defined as a phenomenon whereby transient elastic waves are generated by the rapid release of energy from a localized source or sources within a material [18].…”

Section: Introductionmentioning

confidence: 99%

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Compression damage identification of stitched 2D-C/SiC based on acoustic emission pattern recognition

Zhang

Tong

Lyu

et al. 2021

Journal of Asian Ceramic Societies

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Compression tests of stitched 2D-C/SiC at room temperature were implemented and monitored by acoustic emission (AE). The t-Distributed Stochastic Neighbor Embedding(t-SNE) and the ward-link hierarchical clustering (WLHC) algorithm were used to perform the pattern recognition of the AE data. AE signals were divided into four clusters and labeled by matrix cracking in fiber bundle, matrix cracking between fiber bundles, fiber cluster breakage, interlaminar delamination & friction respectively, according to their physical foundation. It is found:(1) the compression failure process of stitched C/SiC experiences the initial damage stage with a few low-energy AE signals, the damage development stage beginning with high-energy fiber cluster breakage, and the damage acceleration stage in which fiber cluster breakage and interlaminar damage develop rapidly; (2) the macroscopic fracture generally originates from the sparse location of the stitches, and the spacing and uniformity of the stitches have a great impact on the form of fiber breakage and the area of individual delamination damage rather than the direction of the stitches; (3) fiber cluster breakage and interlaminar delamination & friction are the main factors of stitched 2D-C/SiC compressive failure modes, the sum of the corresponding AE events accounts for about 60% of all AE events.

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“…For the stage characteristic of the process, there are some works consider the natural characteristic of the monitored object itself and divide its process into multiple stages or levels for monitoring [36][37][38]. However, in the aspect of real-time abnormal state monitoring, the existing methods of abnormal state monitoring almost based on constructing xed threshold.…”

Section: Introductionmentioning

confidence: 99%

Tool Condition Monitoring in Milling Process Based on Multi-Dource Pattern Recognition Model

Liang

Dai

Huang

et al. 2021

Preprint

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In the milling process of metallic parts, appropriate tool condition is essential to reducing processing faults and ensuring manufacturing quality. However, the existing condition monitoring methods are usually limited by recognizing intermediate abnormal states in milling processing, which is inefficient and impractical for real practical applications. Therefore, this paper proposes a Tool condition monitoring (TCM) method in milling process based on multi-source pattern recognition and state transfer path. Firstly, improved K-Means clustering method is used to generate multiple patterns of tool wear. Secondly, a multi-source pattern recognition model framework is developed, and the multiple observation windows and the pattern transfer path are considered in multi-source pattern recognition model. Lastly, PHM2010 datasets are used to verify the feasibility of the proposed method, and the results demonstrate the applicability of the proposed method in practice for tool condition monitoring.

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Correlation between failure mechanism and rupture lifetime of 2D-C/SiC under stress oxidation condition based on acoustic emission pattern recognition

Cited by 19 publications

References 37 publications

Investigation of the elastic properties of poly (methyl methacrylate) reinforced with graphene nanoplatelets

Investigation of the elastic properties of poly (methyl methacrylate) reinforced with graphene nanoplatelets

Compression damage identification of stitched 2D-C/SiC based on acoustic emission pattern recognition

Tool Condition Monitoring in Milling Process Based on Multi-Dource Pattern Recognition Model

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