This work is aimed to study the effect of a biological medium simulated body fluid on surface modification of titanium alloy Ti6Al4V. Electrochemical techniques and the Raman spectroscopy are used. Polarization curves, plotted at different scan rates (5, 1, 0.5, and 0.2 mV/s), show a large range of passivation (above 3000 mV), with no occurrence of transpassivation. The return sweep shows the negative hysteresis with the shift of the corrosion potential in the more noble direction. The corrosion current is generally very low. These observations clearly confirm the high stability of the TiO2 passive film. The Raman spectroscopy shows that titanium dioxide formed consists of rutile phase. It was found that the scan rate has not a significant effect on the passivation phenomenon. However, it influences the corrosion potential and the current density. Electrochemical impedance spectroscopy allowed to establish the Nyquist diagrams and Bode around the equilibrium potential, and at different imposed potentials in the passivation field. The system behavior is not purely capacitive and so it is necessary to take in consideration the constant phase element. The impedance diagrams at different imposed potentials on the passivation field are composed only of a single loop.

Abstract-The aim of the paper is to present a simple but well applicable development, to generate the waveform of the magnetic flux density, and so the magnetic hysteresis, for any signal frequency. The proposed approach is based on the knowledge of the signal spectrum for one given frequency. It allows to construct the spectrum for any other frequency. Then, the constructed signal is transformed back to the time domain.

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The magnetic behavior of materials is highly sensitive to the applied stress, which is subject of this paper. In magnetostrictive materials, the relationship B(H) is strongly related to the applied mechanical stresses. The purpose of this paper is to present a simple method to identify this relation for any value of stress σ. The proposed approach is based on the knowledge of the magnetic flux density spectrum for different stress σ. So, a feed-forward neural network is trained to learn the relation between the rank of harmonics, stress and the amplitude of each harmonic. To construct the hysteresis loop for a new value of the stress σ, the neural network predicts the spectrum of the magnetic flux density B, which is transformed back to the time domain.