Nano TiB2and TiO2reinforced composites: A comparative investigation on strengthening mechanisms and predicting mechanical properties via neural network modeling

It is known that the interaction between suspended ceramic nanoparticles (TiB 2 and TiO 2 ) 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 TiB 2 and TiO 2 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 nanocomposites in future under different processing conditions

Thermodynamic approach to tailor porosity in piezoelectric polymer fibers for application in nanogenerators

Low power density of polymer piezoelectric nanogenerators is a major hurdle for their application as a potential mode of powering wearable and portable electronic devices. To increase the efficiency, here we suggest use of porous piezoelectric poly (vinylidenefluoride-co-trifluoroethylene)(P(VDF-TrFE))nanofibers. However, designing a process that allows introduction of pores in the nanometric fibers with a diameter of only several 100 nm, is highly challenging due to the intricate physics of polymer/solvent/anti-solvent interactions. Realization of the porous nanofibers would be a breakthrough in the field of piezoelectric nanogenerators. We presents an elegant approach based on the thermodynamics of polymer solutions to tailor porosity in P(VDF-TrFE)nanofibers. By adding a conscious amount of water, carefully chosen as non-solvent based on the ternary phase diagram of P(VDF-TrFE)/water/solvent, we intentionally induce liquid-phase demixing, which leads to formation of nanopores in the electrospun nanofiber. By calculating the mean composition trajectories, we predict and explain formation of the pores in the nanofibers, and show how little variations in initial water content substantially influences fiber porosity. Nanogenerators based on the porous electrospun P(VDF-TrFE)nanofibers show output power that systematically increases with porosity (with 500 times increase in output power for 45% porous fibers). The enhanced output is due to the reduced effective dielectric permittivity of the nanofibers. We unambiguously show that the voltage generation in nanofibers is of the same origin as in neat piezoelectric P(VDF-TrFE)films and is due to the relaxation of segments within the restricted amorphous phase. Understanding how to form nanopores, would have a major contribution to other fields, ranging from nanoporous membranes, as well as porous polymer structures for triboelectric nanogenerators

Boron carbide reinforced aluminium matrix composite : physical, mechanical characterization and mathematical modelling

This paper investigates the manufacturing of aluminium-boron carbide composites using the stir casting method. Mechanical and physical properties tests to obtain hardness, ultimate tensile strength (UTS) and density are performed after solidification of specimens. The results show that hardness and tensile strength of aluminium based composite are higher than monolithic metal. Increasing the volume fraction of B4C, enhances the tensile strength and hardness of the composite; however over-loading of B4C caused particle agglomeration, rejection from molten metal and migration to slag. This phenomenon decreases the tensile strength and hardness of the aluminium based composite samples cast at 800 °C. For Al-15 vol% B4C samples, the ultimate tensile strength and Vickers hardness of the samples that were cast at 1000 °C, are the highest among all composites. To predict the mechanical properties of aluminium matrix composites, two key prediction modelling methods including Neural Network learned by Levenberg-Marquardt Algorithm (NN-LMA) and Thin Plate Spline (TPS) models are constructed based on experimental data. Although the results revealed that both mathematical models of mechanical properties of Al-B4C are reliable with a high level of accuracy, the TPS models predict the hardness and tensile strength values with less error compared to NN-LMA models

Effect of B4C, TiB2 and ZrSiO4 ceramic particles on mechanical properties of aluminium matrix composites: Experimental investigation and predictive modelling

This paper focuses on the influence of processing temperature and inclusion of micron-sized B4C, TiB2 and ZrSiO4 on the mechanical performance of aluminium matrix composites fabricated through stir casting. The ceramic/aluminium composite could withstand greater external loads, due to interfacial ceramic/aluminium bonding effect on the movement of grain and twin boundaries. Based on experimental results, the tensile strength and hardness of ceramic reinforced composite are significantly increased. The maximum improvement is achieved through adding ZrSiO4 and TiB2, which has led to 52% and 125% increase in tensile strength and hardness, respectively. To predict the effect of incorporating ceramic reinforcements on the mechanical properties of composites, experimental data of mechanical tests are used to create 3 models named Levenberg-Marquardt Algorithm (LMA) neural networks. The results show that the LMA- neural networks models have a high level of accuracy in the prediction of mechanical properties for ceramic reinforced-aluminium matrix composites