Structural and Physical Properties Diversity of New CaCu5-Type Related Europium Platinum Borides
Three novel europium platinum borides have been synthesized by arc melting of constituent elements and subsequent annealing. They were characterized by X-ray powder and single-crystal diffraction: EuPt4B, CeCo4B type, P6/mmm, a = 0.56167(2) nm, c = 0.74399(3) nm; Eu3Pt7B2, Ca3Al7Cu2 type as an ordered variant of PuNi3, R3̅m, a = 0.55477(2) nm, c = 2.2896(1) nm; and Eu5Pt18B6–x, a new unique structure type, Fmmm, a = 0.55813(3) nm, b = 0.95476(5) nm, c = 3.51578(2) nm. These compounds belong to the CaCu5 family of structures, revealing a stacking sequence of CaCu5-type slabs with different structural units: CaCu5 and CeCo3B2 type in EuPt4B; CeCo3B2 and Laves MgCu2 type in Eu3Pt7B2; and CaCu5-, CeCo3B2-, and site-exchange ThCr2Si2-type slabs in Eu5Pt18B6–x. The striking motif in the Eu5Pt18B6–x structure is the boron-centered Pt tetrahedron [BPt4], which build chains running along the a axis and plays a decisive role in the structure arrangement by linking the terminal fragments of repeating blocks of fused Eu polyhedra. Physical properties of two compounds, EuPt4B and Eu3Pt7B2, were studied. Both compounds were found to order magnetically at 36 and 57 K, respectively. For EuPt4B a mixed-valence state of the Eu atom was confirmed via magnetic and specific heat measurements. Moreover, the Sommerfeld value of the specific heat of Eu3Pt7B2 was found to be extraordinarily large, on the order of 0.2 J/mol K2.
Magnetic, specific heat and electrical transport properties of Frank–Kasper cage compounds RTM2Al20 [R  =  Eu, Gd and La; TM  =  V, Ti]
Single crystals of Frank–Kasper compounds RTM2Al20 (R  =  Eu, Gd and La; TM  =  V and Ti) were grown by self-flux method and their physical properties were investigated through magnetization (M), magnetic susceptibility (χ), specific heat (CP) and electrical resistivity (ρ) measurements. Powder x-ray diffraction studies and structural analysis showed that these compounds crystallize in the cubic crystal structure with the space group Fd3¯m. The magnetic susceptibility for the compounds EuTi2Al20 and GdTi2Al20 showed a sudden jump below the Néel temperature TN indicative of plausible double magnetic transition. Specific heat (CP) and electrical resistivity (ρ) measurements also confirm the first-order magnetic transition (FOMT) and possible double magnetic transitions. Temperature variation of heat capacity showed a sharp phase transition and huge CP value for the (Eu/Gd)Ti2Al20 compounds’ full width at half-maximum (FWHM) (<0.2 K) which is reminiscent of a first-order phase transition and a unique attribute among RTM2Al20 compounds. In contrast, linear variation of CP is observed in the ordered state for (Eu/Gd)V2Al20 compounds suggesting a λ-type transition. We observed clear anomaly between heating and cooling cycle in temperature-time relaxation curve for the compounds GdTi2Al20 (2.38 K) and EuTi2Al20 (3.2 K) which is indicating a thermal arrest due to the latent heat. The temperature variation of Smag for GdTi2Al20 saturates to a value 0.95Rln⁡8 while the other magnetic systems exhibited still lower entropy saturation values in the high temperature limit. (CP−γT)/T3 versus T plot showed a maximum near 27 K for all the compounds indicating the presence of low frequency Einstein modes of vibrations. Resistivity measurements showed that all the samples behave as normal Fermi liquid type compounds and ρ(T) due to electron–phonon scattering follows Bloch–Grüneisen–Mott relation in the paramagnetic region.
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.