Application of artificial neural networks in atomic force microscopy

References

• E.B. Caldona, A.C.C. De Leon, B.B. Pajarito, and R.C. Advincula, A review on rubber-enhanced polymeric materials, Polym. Rev., vol. 57, no. 2, pp. 311–338, 2017. DOI: 10.1080/15583724.2016.1247102.
   Web of Science ®Google Scholar
• M. Kluppel, The role of disorder in filler reinforcement of elastomers on various length scales, Adv. Polym. Sci., vol. 164, pp. 1–86, 2003. DOI: 10.1007/b11054.
   Web of Science ®Google Scholar
• K. Akutagawa, K. Yamaguchi, A. Yamamoto, H. Heguri, H. Jinnai, and Y. Shinbori, Mesoscopical mechanical analysis of filled elastomer with 3D-finite element analysis and transmission electron microtomography, Rubber Chem. Technol., vol. 81, no. 2, pp. 182–189, 2008. DOI: 10.5254/1.3548203.
   Web of Science ®Google Scholar
• Y. Marco, V. Le Saux, S. Calloch, and P. Charrier, X-ray computed μ-tomography: a tool for the characterization fatigue defect population in a polychloroprene rubber, Procedia Eng., vol. 2, no. 1, pp. 2131–2140, 2010. DOI: 10.1016/j.proeng.2010.03.229.
   Google Scholar
• N.V. Shadrinov and S.I. Nartakhova, Structure and properties of nitrile-butadiene rubber filled with carbon and basalt fibers, Inorg. Mater. Appl. Res., vol. 8, no. 1, pp. 140–144, 2017. DOI: 10.1134/S207511331701035X.
   Google Scholar
• M. Qian, J. Sui, X. Wang, and Y. Zhu, Mechanical properties of silicon carbon black filled natural rubber elastomer, Chem. Res. Chin. Univ., vol. 35, no. 1, pp. 139–145, 2019. DOI: 10.1007/s40242-019-8289-0.
   Web of Science ®Google Scholar
• X. Li Q. Yang, Y. Ye, L. Zhang, S. Hong, N. Ning, and M. Tian, Quantifying 3D-nanosized dispersion of SiO2 in elastomer nanocomposites by 3D-scanning transmission electron microscope (STEM), Compos. Part A, vol. 131, pp. 105778, 2020. DOI: 10.1016/j.compositesa.2020.105778.
   Web of Science ®Google Scholar
• H. Watabe, M. Komura, K. Nakajima, and T. Nishi, Atomic force microscopy of mechanical property of natural rubber, Jpn. J. Appl. Phys., vol. 44, no. 7S, pp. 5393–5396, 2005. DOI: 10.1143/JJAP.44.5393.
   Google Scholar
• O. Lame, Does fractal nanostructure of filled rubber lead to fractal deformations? In situ measurements of strain heterogeneities by AFM, Macromolecules, vol. 43, no. 13, pp. 5881–5887, 2010. DOI: 10.1021/ma100697v.
   Web of Science ®Google Scholar
• D. Gabriel, A. Karbach, D. Drechsler, J. Gutmann, K. Graf, and S. Kheirandish, Bound rubber morphology and loss tangent properties of carbon-black-filled rubber compounds, Colloid Polym. Sci., vol. 294, no. 3, pp. 501–511, 2016. DOI: 10.1007/s00396-015-3802-6.
   Web of Science ®Google Scholar
• D.P. Cole, T.C. Henry, F. Gardea, and R.A. Haynes, Interphase mechanical behavior of carbon fiber reinforced polymer exposed to cyclic loading, Compos. Sci. Technol., vol. 151, pp. 202–210, 2017. DOI: 10.1016/j.compscitech.2017.08.012.
   Web of Science ®Google Scholar
• С.Н. Чвалун, Полимерные нанокомпозиты, Природа., vol. 7, pp. 22–30, 2000. [S.N. Chvalun, Polymer nanocomposites, Priroda, vol. 7, pp. 22–30, 2000].
   Google Scholar
• H.-J. Butt, B. Capella, and M. Kappl, Force measurements with the atomic force microscope: Technique, interpretation and applications, Surf. Sci. Rep., vol. 59, no. 1–6, pp. 1–152, 2005. DOI: 10.1016/j.surfrep.2005.08.003.
   Web of Science ®Google Scholar
• O.K. Garishin and V.V. Moshev, Structural rearrangement in dispersion-filled composites: influence on mechanical properties, Polym. Sci., vol. 47, no. 4, pp. 403–408, 2005.
   Google Scholar
• A.L. Svistkov, A continuum-molecular model of oriented polymer region formation in elastomer nanocomposites, Mech. Solids, vol. 45, no. 4, pp. 562–574, 2010. DOI: 10.3103/S0025654410040060.
   Web of Science ®Google Scholar
• T. Tang and S.D. Felicelli, Computational evaluation of effective stress relaxation behavior of polymer composites, Int. J. Eng. Sci., vol. 90, pp. 76–85, 2015. DOI: 10.1016/j.ijengsci.2015.02.003.
   Web of Science ®Google Scholar
• D. Banerjee, T. Nguyen, and T.-J. Chuang, Mechanical properties of single-walled carbon nanotube reinforced polymer composites with varied interphase’s modulus and thickness: finite element analysis study, Comput. Mater. Sci., vol. 114, pp. 209–218, 2016. DOI: 10.1016/j.commatsci.2015.12.026.
   Web of Science ®Google Scholar
• I. Ivaneiko V. Toshchevikov, M. Saphiannikova, K.W. Stöckelhuber, F. Petry, S. Westermann, and G. Heinrich, Modeling of dynamic-mechanical behavior of reinforced elastomers using a multiscale approach, Polymer, vol. 82, pp. 356–365, 2016. DOI: 10.1016/j.polymer.2015.11.039.
   Web of Science ®Google Scholar
• R. Raghunath, D. Juhre, and M. Klüppel, A physically motivated model for filled elastomers including strain rate and amplitude dependency in finite viscoelasticity, Int. J. Plast., vol. 78, pp. 223–241, 2016. DOI: 10.1016/j.ijplas.2015.11.005.
   Web of Science ®Google Scholar
• P. Kuhn, G. Catalanotti, J. Xavier, P.P. Camanho, and H. Koerber, Fracture toughness and crack resistance curves for fiber compressive failure mode in polymer composites under high rate loading, Compos. Struct., vol. 182, pp. 164–175, 2017. DOI: 10.1016/j.compstruct.2017.09.040.
   Web of Science ®Google Scholar
• M. Malagu, M. Goudarzi, A. Lyulin, E. Benvenuti, and A. Simone, Diameter-dependent elastic properties of carbon nanotube-polymer composites: Emergence of size effects from atomistic-scale simulations, Compos. Part B, vol. 131, pp. 260–281, 2017. DOI: 10.1016/j.compositesb.2017.07.029.
   Web of Science ®Google Scholar
• J. Sapkota, A. Gooneie, A. Shirole, and J.C.M. Garcia, A refined model for the mechanical properties of polymer composites with nanorods having different length distributions, J. Appl. Polym. Sci., vol. 134, no. 36, pp. 45279, 2017. DOI: 10.1002/app.45279.
   Web of Science ®Google Scholar
• M.K. Hassanzadeh-Aghdam, R. Ansari, and F. Darvizeh, Multi-stage micromechanical modeling of effective elastic properties of carbon fiber/carbon nanotube-reinforced polymer hybrid composites, Mech. Adv. Mater. Struct., vol. 26, no. 24, pp. 2047–2061, 2019. DOI: 10.1080/15376494.2018.1472336.
   Web of Science ®Google Scholar
• J. Venetis and E. Sideridis, An upper bound of in-plane shear modulus for unidirectional fibrous composites, Arch. Appl. Mech., vol. 90, no. 1, pp. 173–186, 2020. DOI: 10.1007/s00419-019-01603-3.
   Web of Science ®Google Scholar
• И.А. Морозов, О.К. Гаришин, Ф.В. Володин, А.В. Кондюрин, and С.Н. Лебедев, Экспериментальное и численное моделирование эластомерных композитов путем исследования нанослоев полиизопрена на углеродной поверхности, Механика композиционных материалов и конструкций, vol. 14, №. 1, pp. 3–15, 2008. https://elibrary.ru/item.asp?id=13124280 [I.A. Morozov, O.K. Garishin, F.V. Volodin, A.V. Kondyurin, and S.N. Lebedev, Experimental and numerical modeling of elastomeric composites by studying polyisoprene nanolayers on a carbon surface, Механика композиционных материалов и конструкций, vol. 14, no. 1, pp. 3–15, 2008].
   Google Scholar
• О.К. Гаришин, Моделирование контактного режима работы атомно-силового микроскопа с учетом немеханических сил взаимодействия с поверхностью образца, Вычислительная механика сплошных сред, vol. 5, №. 1, pp. 61–69, 2012. [O.K. Garishin, Simulation of the contact mode of operation of an atomic force microscope, taking into account non-mechanical forces of interaction with the surface of the sample, Comp. Contin. Mech., vol. 5, no. 1, pp. 61–69, 2012]. DOI: 10.7242/1999-6691/2012.5.1.8.
   Google Scholar
• R. Garcia, Nanomechanical mapping of soft materials with the atomic force microscope: methods, theory and applications, Chem. Soc. Rev., vol. 49, no. 16, pp. 5850–5884, 2020. DOI: 10.1039/d0cs00318b.
   Web of Science ®Google Scholar
• F. Tao, H. Zhang, A. Liu, and A.Y.C. Nee, Digital twin in industry: state-of-the-art, IEEE Trans. Ind. Inf., vol. 15, no. 4, pp. 2405–2415, 2019. DOI: 10.1109/TII.2018.2873186.
   Web of Science ®Google Scholar
• D. Jones, C. Snider, A. Nassehi, J. Yon, and B. Hicks, Characterising the digital twin: a systematic literature review, CIRP J. Manuf. Sci. Technol., vol. 29, pp. 36–52, 2020. DOI: 10.1016/j.cirpj.2020.02.002.
   Web of Science ®Google Scholar
• H.F. Stabenau, C.P. Bridge, and J.W. Waks, A novel method of generating augmented annotated electrocardiogram QRST complexes and rhythm strips, Comput. Biol. Med., vol. 134, pp. 104408, 2021. DOI: 10.1016/j.compbiomed.2021.104408.
   PubMed Web of Science ®Google Scholar
• F.X. Wu, M. Li, L. Kurgan, and L. Rueda, Deep neural networks for precision medicine, Neurocomputing, vol. 469, pp. 330–331, 2022. DOI: 10.1016/j.neucom.2021.06.095.
   Web of Science ®Google Scholar
• B.-Y. Ji, Z.-H. You, Y. Wang, Z.-W. Li, and L. Wong, DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding, iScience, vol. 24, no. 6, pp. 102455–102480, 2021. DOI: 10.1016/j.isci.2021.102455.
   PubMed Web of Science ®Google Scholar
• Z. Xu, A. Verma, U. Naveed, S.F. Bakhoum, P. Khosravi, and O. Elemento, Deep learning predicts chromosomal instability from histopathology images, iScience, vol. 24, no. 5, pp. 102394–110421, 2021. DOI: 10.1016/j.isci.2021.102394.
   PubMed Web of Science ®Google Scholar
• K.K. Kumar, Y. Kasiviswanadham, D.V.S.N.V. Indira, P.P. Palesetti, and ChV Bhargavi, Criminal face identification system using deep learning algorithm multi-task cascade neural network (MTCNN), Mater. Today Proc., vol. 80, pp. 2406–2410, 2023. DOI: 10.1016/j.matpr.2021.06.373.
   Google Scholar
• Y.C. Wu, F. Yin, and C.L. Liu, Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models, Pattern Recognit., vol. 65, pp. 251–264, 2017. DOI: 10.1016/j.patcog.2016.12.026.
   Web of Science ®Google Scholar
• T. Parcollet, M. Morchid, G. Linares, and R. De Mori, Bidirectional quaternion long short-term memory recurrent neural networks for speech recognition, International Conference on Acoustics, Speech and Signal Processing, Proceedings., pp. 8519–8523, 2019. https://arxiv.org/pdf/1811.02566.pdf.
   Google Scholar
• J. Bae and Y. Kim, Adaptive controller design for spacecraft formation flying using sliding mode controller and neural networks, J. Franklin Inst., vol. 349, no. 2, pp. 578–603, 2012. DOI: 10.1016/j.jfranklin.2011.08.009.
   Web of Science ®Google Scholar
• D. Perez, B. Wohlberg, T.A. Lovell, M. Shoemaker, and R. Bevilacqua, Orbit-centered atmospheric density prediction using artificial neural networks, Acta Astronaut., vol. 98, pp. 9–23, 2014. DOI: 10.1016/j.actaastro.2014.01.007.
   Web of Science ®Google Scholar
• D. Perez and R. Bevilacqua, Neural Network based calibration of atmospheric density models, Acta Astronaut., vol. 110, pp. 58–76, 2015. DOI: 10.1016/j.actaastro.2014.12.018.
   Web of Science ®Google Scholar
• S. Srivastava, A.K. Roy, and K. Kumar, Design analysis of mixed flow pump impeller blades using ANSYS and prediction of its parameters using artificial neural network, Procedia Eng., vol. 97, pp. 2022–2031, 2014. DOI: 10.1016/j.proeng.2014.12.445.
   Google Scholar
• S.K. Kamane, N.K. Patil, and B.R. Patagundi, Prediction of twisting performance of steel I beam bonded exteriorly with fiber reinforced polymer sheet by using neural network, Mater. Today Proc., vol. 43, pp. 514–519, 2021. DOI: 10.1016/j.matpr.2020.12.026.
   Google Scholar
• R. Srikanth, P. Nemani, and C. Balaji, Multi-objective geometric optimization of a PCM based matrix type composite heat sink, Appl. Energy, vol. 156, pp. 703–714, 2015. DOI: 10.1016/j.apenergy.2015.07.046.
   Web of Science ®Google Scholar
• S. Pathak, R. Zhang, K. Bun, H. Zhang, B. Gayen, and X. Wang, Development of a novel wind to electrical energy converter of passive ferrofluid levitation through its parameter modeling and optimization, Sustain. Energy Technol. Assess., vol. 48, pp. 101641–101648, 2021. DOI: 10.1016/j.seta.2021.101641.
   Web of Science ®Google Scholar
• M. Bessa R. Bostanabad, Z. Liu, A. Hu, D.W. Apley, C. Brinson, W. Chen, and W.K. Liu, A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality, Comput. Methods Appl. Mech. Eng., vol. 320, pp. 633–667, 2017. DOI: 10.1016/j.cma.2017.03.037.
   Web of Science ®Google Scholar
• V.M. Nguyen-Thanh, L.T.K. Nguyen, T. Rabczuk, and X. Zhuang, A surrogate model for computational homogenization of elastostatics at finite strain using the HDMR-based neural network approximator, Preprint arXiv: 1906.02005, pp. 1–42, 2019. DOI: 10.48550/arXiv.1906.02005.
   Google Scholar
• B. Wang, J.H. Ma, and Y.P. Wu, Application of artificial neural network in prediction of abrasion of rubber composites, Mater. Des., vol. 49, pp. 802–807, 2013. DOI: 10.1016/j.matdes.2013.01.047.
   Web of Science ®Google Scholar
• K.-L. Xiang, P.-Y. Xiang, and Y.-P. Wu, Prediction of the fatigue life of natural rubber composites by artificial neural network approaches, Mater. Des., vol. 57, pp. 180–185, 2014. DOI: 10.1016/j.matdes.2013.12.044.
   Web of Science ®Google Scholar
• A.S. Hosseini, P. Hajikarimi, M. Gandomi, F.M. Nejad, and A.H. Gandomi, Optimized machine learning approaches for the prediction of viscoelastic behavior of modified asphalt binders, Constr. Build. Mater., vol. 299, pp. 124264–124278, 2021. DOI: 10.1016/j.conbuildmat.2021.124264.
   Web of Science ®Google Scholar
• S. Kumar, Priyadarshan, and S.K. Ghosh, Statistical and computational analysis of an environment-friendly MWCNT/NiSO4 composite materials, J. Manufac. Process., vol. 66, pp. 11–26, 2021. DOI: 10.1016/j.jmapro.2021.04.001.
   Web of Science ®Google Scholar
• H.-B. Ly, T.-A. Nguyen, H.-V.T. Mai, and V.Q. Tran, Development of deep neural network model to predict the compressive strength of rubber concrete, Constr. Build. Mater., vol. 301, pp. 124081–124100, 2021. DOI: 10.1016/j.conbuildmat.2021.124081.
   Web of Science ®Google Scholar
• Y. Zeng, P. Pan, Z. He, and Z. Shen, An innovative method for axial pressure evaluation in smart rubber bearing based on bidirectional long-short term memory neural network, Measurement, vol. 182, pp. 109653–109662, 2021. DOI: 10.1016/j.measurement.2021.109653.
   Web of Science ®Google Scholar
• M. Zakaulla, Y. Pasha, and S.K. Siddalingappa, Prediction of mechanical properties for polyetheretherketone composite reinforced with graphene and titanium powder using artificial neural network, Mater. Today Proc., vol. 49, no. 5, pp. 1268–1274, 2022. DOI: 10.1016/j.matpr.2021.06.365.
   Google Scholar
• F. Rosetblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Frankfurt Institute for Advanced Studies, p. 616, 1962. DOI:10.1007/978-3-642-70911-1_20
   Google Scholar