Page 65 - IJAMD-1-2

International Journal of AI for
Materials and Design
AI-assisted ML monitoring in additive auxetics

17. Ennaceur C, Laksimi A, Hervé C, Cherfaoui M. Monitoring 27. Qin Y, Singh SC, Grevemeyer I, Marjanović M, Roger
crack growth in pressure vessel steels by the acoustic Buck W. Discovery of flat seismic reflections in the mantle
emission technique and the method of potential difference. beneath the young Juan de Fuca Plate. Nat Commun.
Int J Press Vessels Piping. 2006;83(3):197-204. 2020;11(1):4122.
doi: 10.1016/j.ijpvp.2005.12.004 doi: 10.1038/s41467-020-17946-3
18. Wang JY, Guo JY. Damage investigation of ultra high 28. Yang Z, Yabansu YC, Jha D, et al. Establishing structure-
performance concrete under direct tensile test using acoustic property localization linkages for elastic deformation of
emission techniques. Cem Concr Compos. 2018;88:17-28. three-dimensional high contrast composites using deep
learning approaches. Acta Mater. 2019;166:335-345.
doi: 10.1016/j.cemconcomp.2018.01.007
doi: 10.1016/j.actamat.2018.12.045
19. Wen TK, Yin CC. Crack detection in photovoltaic cells by
interferometric analysis of electronic speckle patterns. Sol 29. Fu T, Monaco F, Li J, et al. Deep-learning-enabled crack
Energy Mater Sol Cells. 2012;98:216-223. detection and analysis in commercial lithium-ion battery
cathodes. Adv Funct Mater. 2022;32(39):2203070.
doi: 10.1016/j.solmat.2011.10.034
doi: 10.1002/adfm.202203070
20. Zarate EA, Custodio GE, Treviño-Palacios CG, Rodríguez-
Vera R, Puga-Soberanes HJ. Defect detection in metals 30. Park D, Park M, Ryu S. Expanding design spaces in digital
using electronic speckle pattern interferometry. Sol Energy composite materials: A multi-input deep learning approach
Mater Sol Cells. 2005;88(2):217-225. enhanced by transfer learning and multi-kernel network.
Adv Theory Simul. 2023;6(11):2300465.
doi: 10.1016/j.solmat.2004.03.009
doi: 10.1002/adts.202300465
21. Liang Z, Qin F, Zheng Y, Zhang Z, Cao W. Noncontact
thermometry based on downconversion luminescence from 31. Ammasai Sengodan G. Prediction of two-phase composite
Eu doped LiNbO single crystal. Sens Actuators A Phys. microstructure properties through deep learning of reduced
3+
3
2016;238:215-219. dimensional structure-response data. Compos B Eng.
2021;225:109282.
doi: 10.1016/j.sna.2015.12.018
doi: 10.1016/j.compositesb.2021.109282
22. Antić Ž, Dramićanin MD, Prashanthi K, Jovanović D,
Kuzman S, Thundat T. Pulsed laser deposited dysprosium- 32. Lee J, Park D, Lee M, et al. Machine learning-based inverse
doped gadolinium–vanadate thin films for noncontact, design methods considering data characteristics and design
self-referencing luminescence thermometry. Adv Mater. space size in materials design and manufacturing: A review.
2016;28(35):7745-7752. Mater Horiz. 2023;10(12):5436-5456.
doi: 10.1002/adma.201601176 doi: 10.1039/d3mh00039g
23. Schlothauer JC, Grabmayer K, Hintersteiner I, Wallner GM, 33. Tan RK, Zhang NL, Ye W. A deep learning-based method for
Röder B. Non-destructive 2D-luminescence detection the design of microstructural materials. Struct Multidiscip
of EVA in aged PV modules: Correlation to calorimetric Optim. 2020;61(4):1417-1438.
properties, additive distribution and a clue to aging doi: 10.1007/s00158-019-02424-2
parameters. Sol Energy Mater Sol Cells. 2017;159:307-317.
34. Li HN, Ren L, Jia ZG, Yi TH, Li DS. State-of-the-art in
doi: 10.1016/j.solmat.2016.09.011 structural health monitoring of large and complex civil
24. Stepanova LV, Yakovleva EM. Asymptotic stress field in the infrastructures. J Civil Struct Health Monit. 2016;6(1):3-16.
vicinity of a mixed-mode crack under plane stress conditions doi: 10.1007/s13349-015-0108-9
for a power-law hardening material. J Mech Mater Struct.
2015;10(3):367-393. 35. Diamanti K, Soutis C. Structural health monitoring
techniques for aircraft composite structures. Prog Aerosp Sci.
doi: 10.2140/jomms.2015.10.367 2010;46(8):342-352.
25. Berto F, Lazzarin P. Recent developments in brittle and quasi- doi: 10.1016/j.paerosci.2010.05.001
brittle failure assessment of engineering materials by means 36. Ye XW, Su YH, Han JP. Structural health monitoring of
of local approaches. Mater Sci Eng R Rep. 2014;75(1):1-48
civil infrastructure using optical fiber sensing technology:
doi: 10.1016/j.mser.2013.11.001 A comprehensive review. Sci World J. 2014;2014:652329.
26. Altenbach H, Hitzler L, Johlitz M, Merkel M, Öchsner A, doi: 10.1155/2014/652329
editors. Lectures Notes on Advanced Structured Materials 2. 37. Li C, He Q, Wang Y, et al. Highly robust and soft
Vol. 203. Switzerland: Springer Nature; 2024.
biohybrid mechanoluminescence for optical signaling and
doi: 10.1007/978-3-031-49043-9 illumination. Nat Commun. 2022;13(1):3914.

Volume 1 Issue 2 (2024) 59 doi: 10.36922/ijamd.3539