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Hofmann, Bulk Metallic Glasses and Their Composites: A Brief History of Diverging Fields, Journal of Materials. 2013 (2013) 1-8
Payer, Corrosion and related mechanical properties of bulk metallic glasses, Journal of Materials Research. 22 (2007) 302-313
Journal of Materials Science, 15 (1980) 1224-1230
Wang, Designing ductile CuZr-based metallic glass matrix composites, Materials Science and Engineering: A, 682 (2016), 542-549
Hosson, Tribological and mechanical properties of high-power laser surface-treated metallic glasses, Materials Science and Engineering: A, 471 (2007), 155-164

Introduction In recent years, in order to fulfil requirements on the load carrying capacity, development of new materials is needed, where ductile materials are very attractive due to their mechanical properties.
Thus, development of new numerical techniques which can describe behavior of ductile materials more realistically, especially in the softening regime has become very popular.
Acknowledgements The work has been supported in part by Croatian Science Foundation under the project “Multiscale Numerical Modeling of Material Deformation Responses from Macro- to Nanolevel” (2516).
Bazant, Journal of engineering mechanics 113(10) (1987) 1512-1533
Geers, et al., International Journal of Plasticity 19(4) (2003) 403-433

Integrated Phase-Field and Machine Learning Study of Microstructure Evolution During Interface-Controlled Spinodal Decomposition Owais Ahmad∗†a, Rakesh Maurya†b, Rajdip Mukherjeec, Somnath Bhowmickd Department of Materials Science and Engineering, Indian Institute of Technology Kanpur, India a*owaisah@iitk.ac.in, brakeshm22@iitk.ac.in, crajdipm@iitk.ac.in, dbsomnath@iitk.ac.in Keywords: Phase-field Model, interface mobility, machine learning, autoencoder, ConvLSTM, microstructure, spinodal decomposition.
This study leverages artificial intelligence (AI) to advance materials science, focusing on microstructural evolution in binary alloys during spinodal decomposition.
The innovative use of an Autoencoder- ConvLSTM model enables precise, low-error microstructural transformation predictions, demonstrating AI’s potential in materials science research.
Materials science is no exception to this trend, with a burgeoning research focus on employing AI for accelerated discoveries.
Hilliard, “Free energy of a nonuniform system. i. interfacial free energy,” The Journal of chemical physics, vol. 28, no. 2, pp. 258–267, 1958

Manna, Glass fibers/SiCp reinforced epoxy composites: Effect of environmental conditions, Journal of Composite Materials. (2017) DOI: 10.1177/0021998317723448
Fascio, Machining of non-conducting materials using electrochemical discharge phenomenon - an overview, International Journal of Machine Tools and Manufacture. 45 (2005) 1095-1108
Fang, Application of genetic algorithm-based fuzzy logic control in wire transport system of wire-EDM machine, Journal of Materials Processing Technology, 205 (2008) 128-137
Manna, A Study on Input Parameters Affecting Material Removal Rate and Surface Roughness in Electrochemical Discharge Machining Process, International Journal of Advance Research in Science and Technology, 3(12) (2014) 400-405
Sorkhel, Experimental investigations into electrochemical discharge machining (ECDM) of non-conductive ceramic material, Journal of Materials Processing Technology, 95 (1999) 145-154

Reference [12] refers to the absorbing materials used in building.
Figure 2 Horizontal polarization versus angle of new materials No.1 Figure 3 Vertical polarization versus angle of new materials No.1 Figure 4 Variation of power reflection coefficient varies to frequency for new materials No.1 With the viewpoint of frequency, bandwidth with power reflection coefficient less than -23 dB is up to 16 GHz.
Parameters of new materials No.2 Floor (mm) 1 2.3-j*0.1 1.042-j*0.055 3.0000 2 4.1-j*0.15 1.2-j*0.05 2.6454 3 18.1-j*3.4 1.7-j*2.2 2.1262 4 3.6-j*0.07 0.991-j*0.187 3.0000 5 21.5-j*1.5 1.6-j*2.5 0.0100 Remar-k Best average (8GHz~12GHz) is: -22.4872dB Figure 5 Horizontal polarization Versus angle of new materials No.2 Figure 6 Vertical polarization versus angle of new materials No.2 Figure 7 Power reflection coefficient versus frequency of new material No.2 Acknowledgment This work was supported in part by Shanghai Municipal Science and Technology Commission under Grant 11DZ2260800。
Mishing, in: Diffusion Processes in Advanced Technological Materials, edtied by D.
Clem: submitted to Journal of Materials Research (2003) Su Donglin, Qi Wanquan, Zhang Lu, Zhanyong, in：Evolutionary algorithm based on genetic inversion of electromagnetic parameters of absorbing structure [6] P.G.

Labbe, Analytical solution of unsteady heat conduction in a two-layered material in imperfect contact subjected to a moving heat source, International Journal of Thermal Sciences, 49 (2010) 311–318
Zhang, Study on Performance of Anisotropic Materials of Thermal Conductivity, The Open Civil Engineering Journal, 5 (2011) 168-172
Echegut, Modeling heat transfer within porous multiconstituent materials, Journal of Physics: Conference Series, 369 (2012) 012001
Hadjes Fandiari, On the symmetric character of the thermal conductivity tensor, International Journal of Materials and Structural Integrity, 8(4), 209, 2014
Abdelaziz, Numerical Solution of Unsteady Conduction Heat Transfer in Anisotropic Cylinders, Journal of Thermal Science and Engineering Applications, 8(3), 031013, 2016

Materials Characterization, 2001, 47:215-218
Chinese Journal of Materials Research, 2002, 16(1):9-12
Materials Science and Engineering, 2001,A304 306:1-10
Materials Science and Engineering A, 2003, A343:194-198
Materials Science Forum, 1999;307-31-6.

Magnesium and its alloys are potential biodegradable implant materials.
Coating materials such as hydroxyapatite (HA) are potential biodegradable coating materials, also helping to accelerate the bone growth [10].
[3] Hazibullah Waizy, Jan-Marten Seitz, Janin Reifenrath, Andreas Weizbauer, Friedrich-Wilhelm Bach, Andrea Meyer-Lindenberg, Berend Denkena, and Henning Windhagen, Biodegradable Magnesium Implants for Orthopedic Applications, Journal of Materials Science, 48 (2013), 39-50
Haung, Electrophoretic Deposition and Its Use to Synthesize Zro2/Al2o3 Micro-Laminate Ceramic/Ceramic Composites, Journal of Materials Science, 28 (1993), 6274-78
Evans, Electrophoretic Deposition of Hydroxyapatite Coatings on Metal Substrates: A Nanoparticulate Dual-Coating Approach, Journal of Sol-Gel Science and Technology, 21 (2001), 39-48

Experiment sample preparation and raw material Raw materials The raw materials: isocyanine (IPDI); prepolymer monomer trimethylol propane triacrylate (TMPTA); photoinitiator: 2-methyl-1-[4-(methylthio)phenyl]-2-(4-morpholinyl)-1-propanone (907), 1-hydroxycyclohexyl phenyl ketone (184); dye precursor 2-anilino-3-methyl-6-dibutylaminofluorane (ODB-2); dispersant (PVA224, PVA217); deionized water; Tetraethylenepentamine; developer 4-hydroxy-4'-isoproxydiphenylsulfone(D-8).
References [1] E N Brown, S R White, N R Sottos, “Microcapsule induced toughening in a self-healing polymer composite,” Journal of Materials Science, vol. 39, no. 5, pp. 1703-1710, March 2004
[2] X W Li, X L Jiang, W D Lai, et al, “Investigation of a Light-thermal Sensitive Imaging System Based on the Microcapsule Technique,” Journal of Imaging Science and Technology, Vol. 51, no. 2, pp. 122-126, February 2007
[4] X Wang, G Q Li, J Wei, W W Guan, “A novel method to control microcapsule release behavior via photo-crosslink polyurethane acrylate shells,” Journal of Applied Polymer Science, vol. 113, no. 2, pp. 1008-1016, July 2009
[6] T Xu, W Yuan, S J Wang, “Synthesis of polyurethane modified bismaleimide(UBMI) and polyurethane-imide elastomer,” Chinese Journal of Polymer Science, vol. 26, no. 1, pp. 117-119, January 2008

Research on the Simulation of Friction Material Surface Topography Run Bo Ma 1,a, Shi Meng Xu 1 and Jian Hua Du 2 1Section of Mathematics, Academy of Armored Force Engineering, Beijing 100072, P.R.China 2Department of Equipment maintenance and Remanufacturing, Academy of Armored Force Engineering, Beijing 100072,P.R.China a13810470589@139.com Key words: composite materials; fractal interpolation; asperity; non-parametric hypothesis test Abstract.
Through analysis and research on the surface topography of reinforced copper matrix composite materials, taking advantage of fractal statistical method to discuss distribution law about characteristics parameter which symbolizes the asperity, combining Monte-Carlo method with fractal theory to set up mathematics model of characteristics parameter which symbolizes size of asperity , talking about the construction of iterated function system in fractal interpolated theory, a easy realizable friction materials surface topography simulation algorithm was put forward.
Let friction material plane is a square with bound length L, noting and as maximum diameter and minimum diameter respectively, and is number of asperity.
In conclusion, friction material surface topography simulation map was given in figure 1.
Journal of Tribology (ASME), 1991, 113: 1–11