Prediction of thermal and mechanical properties of acrylate-based composites using artificial neural network modeling Original scientific paper
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Abstract
Poly(methyl methacrylate) (PMMA) has a broad spectrum of uses, especially in medical applications. The role of fine-grained alumina particles of PMMA composites was investigated in this study. The composites were based on PMMA modified with dimethyl itaconate (DMI) as a matrix and alumina particles (Al2O3) and alumina doped with iron (Al2O3-Fe) modified with
3-aminopropyl-trimethoxysilane (AM) and flax oil fatty acid methyl esters (biodiesel) as reinforcements. Three particle sizes were measured (~0.4, ~0.6 and ~1.2 μm). The highest thermal conductivity values were measured for the composite 5 wt.% Al2O3-Fe-AM. With the addition of 3 wt.% Al2O3-AM to the PMMA/DMI matrix, mechanical properties were improved (tensile strength, strain, and modulus of elasticity). An artificial neural network model based on the Broyden-Fletcher-Goldfarb-Shanno iterative algorithm was investigated for prediction of thermal conductivity and mechanical properties of the composites showing satisfactory results. This is relevant for applications for optimization of dental materials to produce dentures, which were exposed to variations in temperature during the application.
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Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja
Grant numbers 451-03-47/2023-01/20023
References
Kurtz SM, Villarraga ML, Zhao K, Edidin AA. Static and fatigue mechanical behavior of bone cement with elevated barium sulfate content for treatment of vertebral compression fractures. Biomaterials. 2005; 26: 3699-3712. https://doi.org/10.1016/j.biomaterials.2004.09.055
Pascual B, Vtiquez B, Gurruchaga M, Goti I, Ginebra MP, Gil FJ, Planell JA, Levenfeld B, San Romans J. New Aspects of the Effect of Size and Size Distribution on the Setting Parameters and Mechanical Properties of Acrylic Bone Cements. Biomaterials. 1996; 17: 509-516. https://doi.org/10.1016/0142-9612(96)82725-6
Serbetci K, Korkusuz F, Hasirci N. Thermal and Mechanical Properties of Hydroxyapatite Impregnated acrylic bone cements. Polym Test. 2004; 23: 145-155. https://doi.org/10.1016/S0142-9418(03)00073-4
Provenzano MJ, Murphy KPJ, Riley LH. Bone Cements: review of their physiochemical and biochemical properties in percutaneous vertebroplasty. AJNR Am J Neuroradiol. 2004; 25: 1286-1290. http://www.ajnr.org/content/25/7/1286.short
Haas SS, Brauer GM, Dickson GA. Characterization of Poly Methyl Methacrylate Bone Cement. J Bone Joint Surg Am. 1957; 57: 380-391. https://pubmed.ncbi.nlm.nih.gov/1123392/
Postawa P, Szarek A, Koszkul J. DMTA method in determining strength parameters of acrylic cements. Arch Mater Sci Eng. 2007; 28: 309-312. http://www.amse.acmsse.h2.pl/vol28_5/28512.pdf
Khaled SMZ, Charpentier PA, Rizkalla AS. Physical and mechanical properties of PMMA bone cement reinforced with nano-sized titania fibers. J Biomater Appl. 2011; 25: 515-537. https://doi.org/10.1177/0885328209356944
Fernández-Garcia M, Fuente JL, Madruga EL, Thermal behavior of poly(dimethyl itaconate) and poly(di-n-butyl itaconate) copolymerized with methyl methacrylate. Polym Eng Sci. 2001; 41: 1616-1625. https://doi.org/10.1002/pen.10859
EN ISO 20795-1:2013 Dentistry - Base polymers- Part 1: Denture base polymers (ISO 20795-1:2013).
Spasojević P, Stamenković D, Pjanović R, Bošković Vragolović N, Dolić J, Grujić S, Veličković S. Diffusion and solubility of commercial poly(methyl methacrylate) denture base material modified with dimethyl itaconate and di-n-butyl itaconate during water absorption/desorption cycles. Polym Int. 2012; 61: 1272-1278. https://doi.org/10.1002/pi.4202
Alzarrug FA, Dimitrijević MM, Jančić Heinemann RM, Radojević V, Stojanović DB, Uskoković PS, Aleksić R. The use of different alumina fillers for improvement of the mechanical properties of hybrid PMMA composites. Mater Des. 2015; 86: 575-581. https://doi.org/10.1016/j.matdes.2015.07.069
Fu SY, Feng XQ, Lauke B, Mai YW. Effects of particle size, particle/matrix interface adhesion and particle loading on mechanical properties of particulate-polymer composites. Compos B Eng. 2008; 39: 933-961. https://doi.org/10.1016/j.compositesb.2008.01.002
Johnsen BB, Fromyr TR, Thorvaldsen T, Olsen T. Preparation and characterization of epoxy/alumina polymer nanocomposites. Compos Interfaces. 2013; 20: 721-740. https://doi.org/10.1080/15685543.2013.815603
Lazouzi GA, Vuksanović MM, Tomić NZ, Mitrić M, Petrović M, Radojević V, Heinemann RJ. Optimized preparation of alumina based fillers for tuning composite properties. Ceram Int. 2018; 44: 7442-7449. https://doi.org/10.1016/j.ceramint.2018.01.083
Mathieu A, Matteï S, Deschamps A, Martin B, Grevey D. Temperature control in laser brazing of a steel/aluminium assembly using thermographic measurements. NDT E Int. 2006; 39: 272-276. https://doi.org/10.1016/j.ndteint.2005.08.005
Ma J, Wu B. Effect of surfactants on preparation of nanoscale-Al2O3 powders by oil-inwater micro emulsion. Adv Powder Technol. 2013; 24: 354-358. https://doi.org/10.1016/j.apt.2012.08.008
Mališić V, Tomić N, Vuksanović M, Balač B, Stević Z, Marinković A, Jančić-Heinemann R, Putić S. An Experimental study of mechanical properties and heat transfer of acrylic composites with structural and surface modified Al2O3 particles. Sci Sinter. 2020; 52: 457-467. https://doi.org/10.2298/SOS2004457M
Rizzo P, Scalea FL. Acoustic emission monitoring of carbon-fiber-reinforced polymerbridge stay cables in large-scale testing. Exp Mech. 2001; 41: 282-290. https://doi.org/10.1007/BF02323146
Johnson DP, Stanforth A, Lulla V, Luber G. Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data. Appl Geogr. 2012; 35: 23-31. https://doi.org/10.1016/j.apgeog.2012.04.006
Yun TS, Jeong YJ, Han TS, Youm KS. Evaluation of thermal conductivity for thermally insulated concretes. Energy Build. 2013; 61: 125-132. https://doi.org/10.1016/j.enbuild.2013.01.043
Kleijnen JPC. Design and Analysis of Simulation Experiments. Springer, US, 2018; 111-121. https://doi.org/10.1007/978-3-319-76035-3_1
Pavlić B, Pezo L, Marić B, Peić Tukuljac L, Zeković Z, Bodroža Solarov M, Teslić N. Supercritical fluid extraction of raspberry seed oil: Experiments and modelling. J Supercrit Fluids. 2020; 157: 104687. https://doi.org/10.1016/j.supflu.2019.104687
Kollo T, Rosen D. Advanced Multivariate Statistics with Matrices. Springer, Dordrecht, 2005. 98-125. https://link.springer.com/book/10.1007/1-4020-3419-9
Pezo L, Ćurčić BLj, Filipović VS, Nićetin MR, Koprivica GB, Mišljenović NM, Lević LjB. Artificial neural network model of pork meat cubes osmotic dehydratation. Hem Ind. 2013; 67: 465-475. https://doi.org/10.2298/HEMIND120529082P
Ochoa-Martínez CI, Ayala-Aponte AA. Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks. LWT - Food Sci Technol. 2007; 40: 638-645. https://doi.org/10.1016/j.lwt.2006.03.013
Berrueta LA, Alonso-Salces RM, Héberger K. Supervised pattern recognition in food analysis. J Chromatogr. 2007; 1158: 196-214. https://doi.org/10.1016/j.chroma.2007.05.024
Doumpos M, Zopounidis C. Preference disaggregation and statistical learning for multicriteria decision support: A review. Eur J Oper Res. 2011; 209: 203-214. https://doi.org/10.1016/j.ejor.2010.05.029
Taylo BJ. Methods and Procedures for the Verification and Validation of Artificial Neural Networks. Springer Science & Business Media, New York, 2006; 51-67. https://doi.org/10.1007/0-387-29485-6
Yoon Y, Swales G , Margavio TM. A Comparison of Discriminant Analysis versus Artificial Neural Networks. J Oper Res Soc. 1993; 44: 51-60. https://doi.org/10.2307/2584434
Aćimović M, Pezo L, Tešević V, Čabarkapa I, Todosijević M. QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition. Ind Crops Prod. 2020; 154: 112752. https://doi.org/10.1016/j.indcrop.2020.112752
Dos Santos WN, De Sousa JA, Gregorio R. Thermal conductivity behaviour of polymers around glass transition and crystalline melting temperatures. Polym Test. 2013; 32: 987-994. https://doi.org/10.1016/j.polymertesting.2013.05.007
Friederich B, Laachachi A, Ferriol M, Ruch D, Cochez M, Toniazzo V. Tentative links between thermal diffusivity and fire-retardant properties in poly(methyl methacrylate)-metal oxide nanocomposites. Polym Degrad Stab. 2010; 95: 1183-1193. https://doi.org/10.1016/j.polymdegradstab.2010.04.008
Patel T, Suin S, Bhattacharya D, Khatua BB. Transparent and Thermally Conductive Polycarbonate (PC)/Alumina (Al2O3) Nanocomposites: Preparation and Characterizations. Polym Plast Technol Eng. 2013; 52: 1557-1565. https://doi.org/10.1080/03602559.2013.824464
Lazouzi GA, Vuksanović MM, Tomić N, Petrović M, Spasojević P, Radojević V, Jančić R H. Dimethyl Itaconate Modified PMMA ‐ Alumina Fillers Composites With Improved Mechanical Properties. Polym Compos. 2019; 40: 1691-1701. https://doi.org/10.1002/pc.24952
Montgomery DC. Design and Analysis of Experiments.Wiley, 2012; ISBN 9781118146927
Chattopadhyay PB, Rangarajan R. Application of ANN in sketching spatial nonlinearity of unconfined aquifer in agricultural basin. Agric Water Manag. 2014; 133: 81-91. https://doi.org/10.1016/j.agwat.2013.11.007
Erbay Z, Icier F. Optimization of hot air drying of olive leaves using response surface methodology. J Food Eng. 2009; 91: 533-541 https://doi.org/10.1016/j.jfoodeng.2008.10.004