Predicting the catastrophic failure of bulk metallic glasses based on time-series prediction models
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OpenAlex topics: Metallurgical Processes and Thermodynamics · Metallic Glasses and Amorphous Alloys
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References (65)
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1. Mechanical properties of bulk metallic glasses10.1016/j.pmatsci.2010.04.002 ↗
2. Strain-hardening and suppression of shear-banding in rejuvenated bulk metallic glass10.1038/s41586-020-2016-3 ↗
3. Improved mechanical properties and corrosion resistance of Mg-based bulk metallic glass composite by coating with Zr-based metallic glass thin film10.3390/coatings10121212 ↗
4. Application of Zr and Ti-based bulk metallic glasses for orthopaedic and dental device materials10.3390/met10020203 ↗
5. Effects of irradiation spectrum on the microstructural and mechanical properties of bulk metallic glasses10.1016/j.jnucmat.2020.152084 ↗
6. Tailoring the structural heterogeneity and electrochemical behavior of Fe-based bulk metallic glasses by Ar+ irradiation10.1016/j.jallcom.2022.168332 ↗
7. A biocompatible Pd-based BMG with excellent corrosive-wear resistance for implant applications10.1016/j.intermet.2020.106847 ↗
8. Review on biocompatibility and prospect biomedical applications of novel functional metallic glasses10.3390/jfb13040245 ↗
9. A thermo-elastic-plastic phase-field model for simulating the evolution and transition of adiabatic shear band. Part II. Dynamic collapse of thick-walled cylinder10.1016/j.engfracmech.2020.107027 ↗
10. A novel multi-scale large deformation approach for modelling of granular collapse10.1007/s11440-020-01113-5 ↗
11. Mesoscopic-scale complexity in macroscopically-uniform plastic flow of an Al0. 3CoCrFeNi high-entropy alloy10.1016/j.actamat.2022.118445 ↗
12. Compressive behavior and deformation mechanisms of rigid polymeric foams: a review10.1016/j.compositesb.2023.110513 ↗
13. Earthquake, Poisson and Weibull distributions10.1016/j.physa.2019.04.237 ↗
14. Self-organized criticality behavior in bulk metallic glasses10.1016/S1006-706X(16)30003-6 ↗
15. Serrated flow during inhomogeneous bending deformation of bulk metallic glasses: from self-organized critical to chaotic dynamics10.1016/j.jnoncrysol.2018.10.038 ↗
16. Chaotic state to self-organized critical state transition of serrated flow dynamics during brittle-to-ductile transition in metallic glass10.1063/1.4941320 ↗
17. A comparative study of the rate effect on deformation mode in ductile and brittle bulk metallic glasses10.1016/j.intermet.2018.01.020 ↗
18. Effect of deformation geometry on the intermittent plastic flow associated with the Portevin–Le Chatelier effect10.1016/j.actamat.2008.07.025 ↗
19. Stable shear of Cu46Zr47Al7 bulk metallic glass alloy by controlling temperature rise10.1016/j.msea.2010.03.032 ↗
20. Fractal analysis on the uniformity between the shear bands and serrated flows of a Zr-based bulk metallic glass10.1016/j.intermet.2023.107999 ↗
21. Progressive shear band propagation in metallic glasses under compression10.1016/j.actamat.2015.03.026 ↗
22. Aspect ratio effects on the serration dynamics of a Zr-based bulk metallic glass10.1007/s10853-016-0316-7 ↗
23. Shear banding and serrated flow behaviors of high toughness Zr61Ti2Cu25Al12 bulk metallic glass under bending10.1016/j.msea.2022.143172 ↗
24. On the fractal analysis of the serration behavior of a bulk metallic glass composite10.1016/j.jallcom.2021.162605 ↗
25. Shear band velocity and activation volume during shear deformation by acoustic emission in a Zr-based bulk metallic glass10.1016/j.jnoncrysol.2023.122767 ↗
26. Process modeling and optimization in laser drilling of bulk metallic glasses based on GABPNN and machine vision10.1016/j.optlastec.2023.110502 ↗
27. Prediction of glass forming ability of bulk metallic glasses using machine learning10.1007/s40192-021-00239-y ↗
28. Machine learning prediction of glass-forming ability in bulk metallic glasses10.1016/j.commatsci.2021.110362 ↗
29. Prediction of glass forming ability of bulk metallic glasses based on convolutional neural network10.1016/j.jnoncrysol.2022.121846 ↗
30. Remaining useful life prediction via spatio-temporal channels and transformer
31. Multi-scale spatial-temporal aware transformer for traffic prediction10.1016/j.ins.2023.119557 ↗
32. Seismic prediction of porosity in tight reservoirs based on transformer10.3389/feart.2023.1137645 ↗
33. A review of neural networks for air temperature forecasting
34. Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan10.1007/s12145-020-00450-z ↗
35. In-process tool condition forecasting based on a deep learning method10.1016/j.rcim.2019.101924 ↗
36. Earthquake prediction using expert systems: a systematic mapping study10.3390/su12062420 ↗
37. Short-term earthquake prediction in Kamchatka using low-frequency magnetic fields10.1007/s11069-019-03839-2 ↗
38. A stress test to evaluate the usefulness of Akaike information criterion in short-term earthquake prediction10.1038/s41598-020-77834-0 ↗
39. A location-dependent earthquake prediction using recurrent neural network algorithms10.1016/j.soildyn.2022.107389 ↗
40. Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning10.1088/2053-1591/ac24cd ↗
41. A many objective chimp optimization algorithm to de-cluster earthquake catalogs in space time domain10.1007/s00500-023-09314-2 ↗
42. Earthquake prediction using hybrid machine learning techniques
43. Data-driven prediction of thresholded time series of rainfall and self-organized criticality models10.1103/PhysRevE.91.052808 ↗
44. Self-organized criticality and earthquake predictability: a long-standing question in the light of natural time analysis10.1209/0295-5075/132/29001 ↗
45. Identification of self-organized critical state on twitter based on the retweets’ time series analysis10.1155/2021/6612785 ↗
46. ModAugNet: a new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module10.1016/j.eswa.2018.07.019 ↗
47. Stock linkage prediction based on optimized LSTM model10.1007/s11042-022-12381-6 ↗
48. An attention-based LSTM network for large earthquake prediction10.1016/j.soildyn.2022.107663 ↗
49. Short-term wind speed interval prediction based on ensemble GRU model10.1109/TSTE.2019.2926147 ↗
50. Water level prediction model based on GRU and CNN10.1109/ACCESS.2020.2982433 ↗
51. State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer10.1016/j.energy.2023.129401 ↗
52. Swinlstm: improving spatiotemporal prediction accuracy using swin transformer and lstm
53. Connecting shear localization with the long-range correlated polarized stress fields in granular materials10.1038/s41467-020-18217-x ↗
54. On the wire EDM of metastable atomic structured bulk metallic glasses10.1007/s00170-022-09068-5 ↗
55. Mechanical property and serration behavior of Ti-based metallic glassy composites reinforced by an in-situ dendritic phase10.1016/j.msea.2018.11.090 ↗
56. Serrated flow and stick–slip deformation dynamics in the presence of shear-band interactions for a Zr-based metallic glass10.1016/j.actamat.2012.04.013 ↗
57. Low-speed machining of a Zr-based bulk metallic glass10.1016/j.jmapro.2021.10.055 ↗
58. Hochreiter · 1997
59. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, Attention is all you need.(Nips), 2017, arxiv preprint arxiv:1706.03762 10 (2017) S0140525 × 16001837, https://doi.org/10.48550/arXiv.1706.03762.
60. K. Cho, On the properties of neural machine translation: encoder-decoder approaches, arxiv preprint arxiv:1409.1259 (2014), https://doi.org/10.3115/v1/W14-4012.10.3115/v1/W14-4012 ↗
61. On the relationship between the accumulation and release of elastic energy during the flow serrations of a Zr-based bulk metallic glass10.1016/j.msea.2021.141423 ↗
62. Prediction of total electron content (TEC) using neural network over anomaly crest region Bhopal10.1016/j.asr.2021.05.027 ↗
63. Combining LSTM network ensemble via adaptive weighting for improved time series forecasting
64. Processing of monolithic bulk metallic glass using sinking electrical discharge machining10.1007/s00170-023-11459-1 ↗
65. Electropolishing of complex-shaped bulk metallic glasses in NaCl-ethylene glycol electrolyte