High-Moment-Low-Moment Description of Magnetovolume Effects in Y(Mn x Al 1− x ) 2 and Y x Sc 1− x Mn 2
Based on the assumption of a high-moment-low-moment instability of the Mn atom, we construct a simple spin model with coupled magnetic and spatial degrees of freedom to describe the Laves phase systems Y(Mn x Al 1 m x ) 2 and Y x Sc 1 m x Mn 2 . Monte Carlo simulations of this model qualitatively reproduce anomalies observed in these materials like a discontinuous giant volume change and anomalous thermal expansion behavior.
Simulation of the (p, T) phase diagram of the temperature-driven metamagnet α-FeRh
We perform finite-pressure Monte Carlo simulations of an effective spin-analogous model with coupled magnetic and lattice degrees of freedom, which has previously been proposed in order to explain the anomalous temperature driven metamagnetic phase transition in α-FeRh. The results are in reasonable agreement with the experimental (p, T) phase diagram. The critical behaviour of the system along the transition lines is analysed in detail.
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.