Synthesis and Magnetostrictive Properties of High-Pr Content  ${\rm Sm}_{1-x}{\rm Pr}_{x}{\rm Fe}_{1.9}$ Cubic Laves Alloys
Polycrystalline Sm<sub>1-</sub><i>x</i>Pr<i>x</i>Fe<sub>1.9</sub> (0.4 ≤ <i>x</i> ≤ 1.0) magnetostrictive alloys were synthesized by arc-melting and subsequent high-pressure annealing. Measurements of x-ray diffraction, magnetization, and magnetostriction were performed on the alloys. Due to the effects of high-pressure, the alloys are stabilized in cubic Laves phase with MgCu<sub>2</sub>-type structure. The Curie temperature of the cubic Laves phase decreases linearly with the increase of <i>x</i>, which can be associated with the decreasing strength of 3<i>d</i>-4<i>f</i> coupling. With increasing <i>x</i> , the anisotropy of the alloys first decreases to a minimum at <i>x</i> = 0.6 and then increases. The magnetostriction of Sm<sub>1-</sub><i>x</i>Pr<i>x</i>Fe<sub>1.9</sub> changes signs from negative into a positive at <i>x</i> = 0.6 . The saturation magnetostriction increases with increasing <i>x</i>, while the sample with <i>x</i> = 0.9 shows a peak at low magnetic fields.
Temperature dependence of the magnetostriction in SmxDy1−xFe2 compounds
The temperature dependence of the magnetostriction from 80–300 K, as a function of applied field, was investigated in a series of SmxDy1−xFe2 (x = 0.86, 0.70, 0.55, 0.40, and 0.20) polycrystalline compounds. Large strains are observed in Sm0.86Dy0.14Fe2 with λ∥−λ⊥ = −1688×10−6 at room temperature and −2725 ppm at 80 K, as the large negative λ111 found in SmFe2 dominates. The competing anisotropies of SmFe2 and DyFe2 compensate near room temperature in this composition, permitting the magnetostriction at 300 K to saturate at 7 kOe. As one proceeds across the series, the influence of DyFe2 causes a decrease in ‖λ‖ with λ∥−λ⊥ = −77×10−6 at 300 K in Sm0.55Dy0.45Fe2. The field dependence at lower temperatures indicates a rotation of the magnetic moment, resulting in a positive dλ/dH in the high field portion of the curves. The magnetostriction of Sm0.20Dy0.80Fe2 initially shows a negative strain which changes sign as the field is increased. The field curves are far from saturation at all temperatures with λ∥−λ...
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.