Acoustic emission analysis for damage mode identification and damage evolution of carbon fiber-reinforced ceramic matrix composites
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Advanced ceramic materials synthesis · Smart Materials for Construction · Fiber-reinforced polymer composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Vigliotti A, Auricchio F, Pasini D (2025) On the role of the matrix in the strength of carbon fiber-reinforced ceramics. Mech Mater 201:105227. https://doi.org/10.1016/j.mechmat.2024.10522710.1016/j.mechmat.2024.105227 ↗
2. Tracy J, Daly S, Sevener K (2015) Multiscale damage characterization in continuous fiber ceramic matrix composites using digital image correlation. J Mater Sci 50:5286–5299. https://doi.org/10.1007/s10853-015-9076-z10.1007/s10853-015-9076-z ↗
3. Ma Y, Song H, Niu X, Chen Y, Suo T (2024) Effect of fiber roughness on interfacial friction of fiber reinforced ceramic matrix composites. Ceram Int 50:7086–7100. https://doi.org/10.1016/j.ceramint.2023.12.06610.1016/j.ceramint.2023.12.066 ↗
4. Xu Q, Xiao S, Wang Y-Q, Gao H (2024) A review on the grinding of SiC-based ceramic matrix composites reinforced by continuous fibre: damage mechanisms and evaluations. J Manuf Process 132:261–295. https://doi.org/10.1016/j.jmapro.2024.10.06710.1016/j.jmapro.2024.10.067 ↗
5. Ramachandran K, Boopalan V, Bear JC, Subramani R (2022) Multi-walled carbon nanotubes (MWCNTs)-reinforced ceramic nanocomposites for aerospace applications: a review. J Mater Sci 57:3923–3953. https://doi.org/10.1007/s10853-021-06760-x10.1007/s10853-021-06760-x ↗
6. Chikhradze NM, Marquis FDS, Abashidze GS (2015) Hybrid fiber and nanopowder reinforced composites for wind turbine blades. J Mater Res Technol 4:60–67. https://doi.org/10.1016/j.jmrt.2015.01.00210.1016/j.jmrt.2015.01.002 ↗
7. Yang C, Yang L, Xu P, Guo W, Li P, Ma Y et al (2024) Mechanical and microstructure characterisation of 2.5D C/C-SiC composites applied for the brake disc of high-speed train. Alex Eng J 108:445–458. https://doi.org/10.1016/j.aej.2024.07.13210.1016/j.aej.2024.07.132 ↗
8. Dong H, Gao X, Zhang S, Yu G, Song Y, Wang F (2021) Multi-scale modeling and experimental study of fatigue of plain-woven SiC/SiC composites. Aerosp Sci Technol 114:106725. https://doi.org/10.1016/j.ast.2021.10672510.1016/j.ast.2021.106725 ↗
9. Builo SI, Builo BI, Chebakov MI (2021) Probalistic-information approach to assessing the reliability of the results of the acoustic-emission method of testing and diagnostics. Russ J Nondestruct Test 57:375–382. https://doi.org/10.1134/S106183092105007710.1134/S1061830921050077 ↗
10. Xu F, Wu J, Hong D, Zhao F, Wu J, Yan J, Hu W (2025) A lightweight hybrid model-based condition monitoring method for grinding wheels using acoustic emission signals. Meas Sci Technol 36:016145. https://doi.org/10.1088/1361-6501/ad8ee510.1088/1361-6501/ad8ee5 ↗
11. Chai M, Gao Z, Li Y, Zhang Z, Duan Q, Chen R (2022) An approach for identifying corrosion damage from acoustic emission signals using ensemble empirical mode decomposition and linear discriminant analysis. Meas Sci Technol 33:065018. https://doi.org/10.1088/1361-6501/ac4ed710.1088/1361-6501/ac4ed7 ↗
12. Wu Q, Shi X, Jiao F, Li W, Ti X, Liu H (2024) Three-point bending damage detection of SiC coated C/C composites based on acoustic emission. Mater Today Commun 41:110655. https://doi.org/10.1016/j.mtcomm.2024.11065510.1016/j.mtcomm.2024.110655 ↗
13. Shu W, Liao L, Zhou P, Huang B, Chen W (2023) Three-point bending damage detection of GFRP composites doped with graphene oxide by acoustic emission technology. iScience 26:108511. https://doi.org/10.1016/j.isci.2023.10851110.1016/j.isci.2023.108511 ↗
14. Lyu P, Yao L, Ma X, An G, Bai G, Augousti AT et al (2020) Correlation between failure mechanism and rupture lifetime of 2D-C/SiC under stress oxidation condition based on acoustic emission pattern recognition. J Eur Ceram Soc 40:5094–5102. https://doi.org/10.1016/j.jeurceramsoc.2020.06.07010.1016/j.jeurceramsoc.2020.06.070 ↗
15. Shi Y, He Q, Wang A, Wang H, Wang W, Fu Z (2025) Anisotropies in microstructure and properties of Si3N4/BN composite ceramics: synergistic effect of bimodal texture. Mater Sci Eng A 930:148162. https://doi.org/10.1016/j.msea.2025.14816210.1016/j.msea.2025.148162 ↗
16. Yang H, Zhao G, Xia H, Xin L, Nian Z, Li L (2024) Effect of fiber orientation on material removal mechanisms in the machining of 3D Cf/SiC composite with a brazed diamond grinding rod: experiment and simulation. J Manuf Process 114:18–38. https://doi.org/10.1016/j.jmapro.2024.01.06610.1016/j.jmapro.2024.01.066 ↗
17. Zhang P, Zhu L, Tong Y, Li Y, Xing Y, Lan H et al (2024) Revealing thermal shock behaviors and damage mechanism of 3D needled C/C–SiC composites based on multi-scale analysis. J Mater Res Technol 29:2016–2034. https://doi.org/10.1016/j.jmrt.2024.01.26010.1016/j.jmrt.2024.01.260 ↗
18. Zhao W, Yu R, Dong W, Luan J, Wang G, Zhang H et al (2021) The influence of long carbon fiber and its orientation on the properties of three-dimensional needle-punched CF/PEEK composites. Compos Sci Technol 203:108565. https://doi.org/10.1016/j.compscitech.2020.10856510.1016/j.compscitech.2020.108565 ↗
19. Fan S, Zhang L, Xu Y, Cheng L, Lou J, Zhang J et al (2007) Microstructure and properties of 3D needle-punched carbon/silicon carbide brake materials. Compos Sci Technol 67:2390–2398. https://doi.org/10.1016/j.compscitech.2007.01.00810.1016/j.compscitech.2007.01.008 ↗
20. Rameshchandra SM, Dikshit MK, Kumar A, Vora JJ, Panchal SM, Bandhu D (2024) A novel intelligent tool wear monitoring system in ball end milling of Ti6Al4V alloy using artificial neural network. High Temp Mater Processes 43:20240020. https://doi.org/10.1515/htmp-2024-002010.1515/htmp-2024-0020 ↗
21. Srinag T, Srinivas CL, Sandeep CH, Prasanthi PP, Madhav VVV, Kumar A, Chandrashekar R, Bandhu D, Singh SK, Lakshmi AA (2025) Mechanical characterization of walnut shell powder-reinforced neem shell liquid composite. J Mech Behav Mater 34:20250078. https://doi.org/10.1515/jmbm-2025-007810.1515/jmbm-2025-0078 ↗
22. Fuse K, Venkata P, Reddy RM, Bandhu D (2025) Machine learning classification approach for predicting tensile strength in aluminium alloy during friction stir welding. Int J Interact Des Manuf 19:639–643. https://doi.org/10.1007/s12008-024-01999-510.1007/s12008-024-01999-5 ↗
23. Prasanthi PP, Ramacharyulu DA, Babu KS, Madhav VVV, Chaitanya CS, Saxena KK, Revathi V, Abduvalieva D, Bandhu D (2025) Role of fiber orientation and design on thermal and mechanical properties of natural composite. Int J Interact Des Manuf 19:1383–1393. https://doi.org/10.1007/s12008-024-02042-310.1007/s12008-024-02042-3 ↗
24. Fan S, Ning Y, Ma X, Wang L, Deng J, Zhang L, Cheng L (2019) Microstructure and mechanical properties of Fe–Si alloy modified C/C–SiC composites. Ceram Int 45:21579–21589. https://doi.org/10.1016/j.ceramint.2019.07.15310.1016/j.ceramint.2019.07.153 ↗
25. Xiao P, Lu Y, Liu Y, Li Z, Fang H, Zhou W, Almeida RSM, Li Y (2017) Microstructure and properties of Cu-Ti alloy infiltrated chopped Cf reinforced ceramics composites. Ceram Int 43:16628–16637. https://doi.org/10.1016/j.ceramint.2017.09.05310.1016/j.ceramint.2017.09.053 ↗
26. Tong Y, Wang B, Hua M, Hu Y, Han K, Shen H, Li Y, Zhang X, Zhang P, Liu Y (2022) Tough and wear-resistant carbon fiber reinforced TiC-based composite prepared by alloyed melt infiltration at low temperatures. Ceram Int 48:4665–4674. https://doi.org/10.1016/j.ceramint.2021.11.00210.1016/j.ceramint.2021.11.002 ↗
27. Wen X, Guo L, Bao Q, Guo Z (2020) Fabrication of TiC from the Cu–Ti–C system under the super-gravity field. J Alloy Compd 832:154995. https://doi.org/10.1016/j.jallcom.2020.15499510.1016/j.jallcom.2020.154995 ↗
28. Xu Z, Wang X, Liu Y, Xu B, Wang B (2024) Fracture failure analysis of plastic fiber based on thermodynamic strength theory and experiment. Eng Fract Mech 310:110505. https://doi.org/10.1016/j.engfracmech.2024.11050510.1016/j.engfracmech.2024.110505 ↗
29. Xu J, Wang W, Han Q, Liu X (2020) Damage pattern recognition and damage evolution analysis of unidirectional CFRP tendons under tensile loading using acoustic emission technology. Compos Struct 238:111948. https://doi.org/10.1016/j.compstruct.2020.11194810.1016/j.compstruct.2020.111948 ↗
30. Yu X, Montrésor S, Bentahar M, Mechri C (2023) Cluster analysis of acoustic emission signals for the damage pattern recognition of polymer concrete. Appl Acoust 211:109533. https://doi.org/10.1016/j.apacoust.2023.10953310.1016/j.apacoust.2023.109533 ↗
31. Pashmforoush F, Fotouhi M, Najafabadi MA (2012) Acoustic emission-based damage classification of glass/polyester composites using harmony search k-means algorithm. J Reinf Plast Compos 31:671–680. https://doi.org/10.1177/073168441244225710.1177/0731684412442257 ↗
32. Hu H, Liu J, Zhang X, Fang M (2023) An effective and adaptable K-means algorithm for big data cluster analysis. Pattern Recognit 139:109404. https://doi.org/10.1016/j.patcog.2023.10940410.1016/j.patcog.2023.109404 ↗
33. Heidari J, Daneshpour N, Zangeneh A (2024) A novel K-means and K-medoids algorithms for clustering non-spherical-shape clusters non-sensitive to outliers. Pattern Recognit 155:110639. https://doi.org/10.1016/j.patcog.2024.11063910.1016/j.patcog.2024.110639 ↗
34. Zhang Y, Xiong A, Xiao Y, Chen Z (2023) A new combination method based on Pearson coefficient and information entropy for multi-sensor data fusion. Inf Softw Technol 161:107248. https://doi.org/10.1016/j.infsof.2023.10724810.1016/j.infsof.2023.107248 ↗
35. Khan IK, Daud HB, Zainuddin NB, Sokkalingam R, Farooq M, Baig ME et al (2024) Determining the optimal number of clusters by enhanced gap statistic in K-mean algorithm. Egypt Inform J 27:100504. https://doi.org/10.1016/j.eij.2024.10050410.1016/j.eij.2024.100504 ↗
36. Ri YA, Kang CR, Kim KH, Choe YM, Han UC (2022) A new method to determine cluster number without clustering for every K based on ratio of variance to range in K-Means. Math Probl Eng 2022:1–14. https://doi.org/10.1155/2022/686674710.1155/2022/6866747 ↗
37. Ros F, Riad R, Guillaume S (2023) PDBI: a partitioning Davies-Bouldin index for clustering evaluation. Neurocomputing 528:178–199. https://doi.org/10.1016/j.neucom.2023.01.04310.1016/j.neucom.2023.01.043 ↗
38. Su X, Shang S, Xu Z, Qian H, Pan X (2024) Assessment of dependent performance shaping factors in SPAR-H based on Pearson correlation coefficient. CMES 138:1813–1826. https://doi.org/10.32604/cmes.2023.03095710.32604/cmes.2023.030957 ↗
39. Zhang J, Li H, Yang X, Cheng Z, Zou PXW, Gong J et al (2024) A novel moisture damage detection method for asphalt pavement from GPR signal with CWT and CNN. NDT E Int 145:103116. https://doi.org/10.1016/j.ndteint.2024.10311610.1016/j.ndteint.2024.103116 ↗
40. Shen C-H (2022) Acoustic emission based grinding wheel wear monitoring: signal processing and feature extraction. Appl Acoust 196:108863. https://doi.org/10.1016/j.apacoust.2022.10886310.1016/j.apacoust.2022.108863 ↗
41. Ni Y, Luo R, Luo H (2019) Fabrication and mechanical properties of 3-D Cf/C-SiC-TiC composites prepared by RMI. J Alloy Compd 798:784–789. https://doi.org/10.1016/j.jallcom.2019.05.19710.1016/j.jallcom.2019.05.197 ↗
42. Tong Y, Wang B, Hu Y, Hua M, Han K, Shen H et al (2022) Effect of Ti–Cu alloy’s composition on microstructure and performance of carbon fiber reinforced TiC-based composites prepared by alloyed reactive melt infiltration. Ceram Int 48:30555–30563. https://doi.org/10.1016/j.ceramint.2022.06.33610.1016/j.ceramint.2022.06.336 ↗