Temperature-dependent grain size prediction model for Nimonic 80A superalloy based on multi-feature information of laser ultrasonic and Bayesian neural network
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OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Additive Manufacturing Materials and Processes · Metallurgy and Material Forming
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References (57)
Show source-provided references
1. Experimental Investigation on Micro-Electrical Discharge Machining process for heat treated Nickel-based Nimonic 80A10.1080/10426914.2022.2105889 ↗
2. State-of-the-art in surface integrity in machining of nickel-based super alloys10.1016/j.ijmachtools.2015.10.001 ↗
3. Microstructure evolution during dynamic recrystallisation in polycrystalline nickel superalloys10.1016/j.msea.2015.03.121 ↗
4. Prediction of grain refinement using multiscale crystal plasticity-integrated dislocation density-based model in multiphase steel alloys10.1016/j.commatsci.2024.113026 ↗
5. In-situ measurement of Ti-6Al-4V grain size distribution using laser-ultrasonic technique10.1016/j.scriptamat.2018.05.009 ↗
6. The effect of grain size and anomalous shape on low cycle fatigue of nickel-based superalloy at elevated temperature10.1016/j.ijfatigue.2024.108526 ↗
7. Crystal plasticity framework related to size effect: from single crystal parameters to polycrystalline mechanical properties10.1016/j.msea.2024.146747 ↗
8. Seventy years of Hall-Petch, ninety years of superplasticity and a generalized approach to the effect of grain size on flow stress10.1016/j.pmatsci.2023.101131 ↗
9. Reactive wear protection through strong and deformable oxide nanocomposite surfaces
10. Microstructure and mechanical properties of the Ag/316L composite plate fabricated by explosive welding10.1016/j.jmapro.2021.01.026 ↗
11. Plastic deformation behavior of a nickel-based superalloy on the mesoscopic scale10.1016/j.jmst.2019.09.020 ↗
12. Effects of grain refinement on the low-cycle fatigue behavior of IN792 superalloys10.3390/cryst11080892 ↗
13. Accuracy assessment of crystal orientation indexations by EBSD10.1088/1361-6501/ad204d ↗
14. The preparation, preparation mechanism and properties of extra coarse-grained WC–Co hardmetals10.1016/j.mprp.2017.01.001 ↗
15. The uncertainty in the grain size calculation from X-ray diffraction data10.1080/01411594.2012.715301 ↗
16. Method for determining crystal grain size by X-Ray diffraction10.1002/crat.201700157 ↗
17. High-performance ultrasonic transducer based on PZT piezoelectric ceramic for high-temperature NDE10.1016/j.ultras.2023.107013 ↗
18. Characterization of internal defects in cylindrical components using laser ultrasonic method with a modified SAFT algorithm10.1016/j.ndteint.2024.103243 ↗
19. Thickness inversion of thin additive manufacturing Ti-6Al-4 V based on dispersion curve variation with laser-induced ultrasonic10.1016/j.ultras.2025.107629 ↗
20. Non-contact measurements of residual stress distribution and grain size in titanium alloys with laser ultrasonic system10.1016/j.ijmecsci.2023.108809 ↗
21. Grain size characterization method of dual-phase titanium alloys with complex microstructure based on laser ultrasonic10.1016/j.measurement.2023.112670 ↗
22. Grain size characterization by laser-based ultrasonics based on the centroid frequency shift method10.1016/j.matchar.2019.109800 ↗
23. Characterization of metal grain size using broadband laser ultrasonic technology10.1016/j.measurement.2025.117371 ↗
24. In-situ laser ultrasonic grain size measurement in superalloy INCONEL 71810.1016/j.jallcom.2016.01.222 ↗
25. In-situ austenite grain growth measurements in an X80 line pipe steel10.1016/j.matchar.2025.114998 ↗
26. Grain size distribution characterization of aluminum with a particle swarm optimization neural network using laser ultrasonics10.1016/j.apacoust.2021.108125 ↗
27. Grain size characterization of Ti-6Al-4V titanium alloy based on laser ultrasonic random forest regression10.1364/AO.479323 ↗
28. The microstructure characterization of a titanium alloy based on a laser ultrasonic random Forest regression10.3390/cryst14070607 ↗
29. Laser-ultrasonic evaluation of grain size numbers in laser metal deposited Ti6Al4V with nano-sized B4C using convolutional neural network and complex Morlet continuous wavelet transform10.1016/j.measurement.2024.116450 ↗
30. What uncertainties do we need in bayesian deep learning for computer vision?
31. Uncertainty quantification using neural networks for molecular property prediction10.1021/acs.jcim.0c00502 ↗
32. Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks
33. Modelling the relationship between process parameters and mechanical properties using Bayesian neural networks for powder metal parts10.1080/00207540050028052 ↗
34. Bayesian neural networks for uncertainty quantification in data-driven materials modeling10.1016/j.cma.2021.114079 ↗
35. Prediction of grain size distribution in microstructure of polycrystalline materials using one dimensional convolutional neural network (1D-CNN)10.1016/j.commatsci.2023.112416 ↗
36. Laser-Ultrasound-Based grain size gauge for the hot strip mill10.3390/app121910048 ↗
37. Directional dependence of aluminum grain size measurement by laser-ultrasonic technique10.1016/j.matchar.2017.04.027 ↗
38. Grain size characterization of 5A06 aluminum alloy plates with different deformation amounts based on laser ultrasonic10.1016/j.measurement.2024.115343 ↗
39. Evolution of austenite recrystallization and grain growth using laser ultrasonics10.1007/s11661-007-9461-6 ↗
40. A new technique for the quantitative real-time monitoring of austenite grain growth in steel10.1016/S1359-6462(00)00305-5 ↗
41. Direct measurements of grain size in low-carbon steels using the laser ultrasonic technique10.1007/s11661-002-0131-4 ↗
42. Determination of grain size in deep drawing steel sheet by laser ultrasonics10.2320/matertrans.I-M2014808 ↗
43. Uncertainty quantification in multiaxial fatigue life prediction using Bayesian neural networks10.1016/j.engfracmech.2024.109961 ↗
44. Jospin
45. Electron beam welding of nimonic 80A superalloy: microstructure evolution and EBSD Study after aging heat treatment10.1007/s11665-018-3817-5 ↗
46. Deformation and microstructure radial gradient evolution of AZ31 magnesium alloy bar during three-roll skew rolling10.1016/j.jallcom.2024.176331 ↗
47. Effect of Nb content on the abnormal grain growth in 20MnCr5 hot‐rolled bar10.1002/srin.202400487 ↗
48. Chen · 2012
49. Thermal behavior of a rod during hot shape rolling and its comparison with a plate during flat rolling10.3390/pr8030327 ↗
50. Recrystallization behaviour of the nickel-based alloy 80 A during hot forming10.1111/j.1365-2818.2007.01810.x ↗
51. Response to hot deformation conditions and microstructure development of nimonic 80A superalloy10.1080/10426910902769103 ↗
52. Mechanical properties measurement of chromium coatings based on laser ultrasonic technology in high-temperature conditions10.1016/j.ymssp.2025.112421 ↗
53. Laser ultrasonic inspection for mechanical properties of materials at high temperature10.1063/5.0147615 ↗
54. Advances in postprocessing technology for laser ultrasound detection signals: a review10.1109/JSEN.2023.3325490 ↗
55. Correlation processor based Sidelobe suppression for polyphase codes in radar systems10.1007/s11277-020-07576-9 ↗
56. High fidelity sampling schedules for NMR spectra of high dynamic range10.1016/j.jmr.2022.107228 ↗
57. A survey of uncertainty in deep neural networks10.1007/s10462-023-10562-9 ↗