Nano TiB2 and TiO2 reinforced composites: A comparative investigation on strengthening mechanisms and predicting mechanical properties via neural network modeling

被引:24
作者
Akbari, Mohammad Karbalaei [1 ]
Shirvanimoghaddam, Kamyar [2 ]
Hai, Zhenyin [1 ]
Zhuiykov, Serge [1 ]
Khayyam, Hamid [3 ]
机构
[1] Univ Ghent, Fac Biosci, Global Campus, Incheon, South Korea
[2] Deakin Univ, Inst Frontier Mat, Carbon Nexus, Geelong, Vic, Australia
[3] RMIT Univ, Sch Engn, Melbourne, Vic 3000, Australia
关键词
TiB2; TiO2; Nanocomposites; Wettability; Neural network modeling; METAL-MATRIX COMPOSITES; ALUMINUM-MATRIX; MOLTEN ALUMINUM; MICROMECHANICAL MODEL; POWDER-METALLURGY; TITANIUM DIBORIDE; ENERGY MANAGEMENT; FRACTURE-BEHAVIOR; VOLUME FRACTION; WEAR BEHAVIOR;
D O I
10.1016/j.ceramint.2017.09.077
中图分类号
TQ174 [陶瓷工业]; TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
It is known that the interaction between suspended ceramic nanoparticles (TiB2 and TiO2) in molten alloys affects the strengthening mechanisms of nanoparticle reinforced composites. The present study follows a comparative approach to investigate this phenomenon during casting process. of aluminum composites reinforced by TiB2 and TiO2 nanoparticles. Microstructural studies accompanied by the measurements of hardness and tensile strength showed that the highest improvement in mechanical properties of the nanocomposites was achieved when Orowan, load bearing mechanism and Hall-Petch mechanisms were simultaneously engaged in the strengthening process of the metal matrix. In order to predict the mechanical properties, four artificial neural networks based on multi-input and multi-output (MIMO) and single-input and multi-output (SIMO) models were created using Bayesian regularization, and cross validated. Showing errors less than 5%, the developed models can reliably be used to reduce the product development time and fabrication of the aluminum matrix nano composites in future under different processing conditions.
引用
收藏
页码:16799 / 16810
页数:12
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