Failure analysis of Sm2Co17 type permanent magnet for high temperature
With the increasing using temperature, the magnetic performance of high temperature stable Sm2Co17 type permanent magnet will be decreased. In order to explore the effect of the high temperature on microstructure of the alloy, the differential thermal analysis (DTA) was made and three distinct phase transformation temperature 254 °C, 342 °C and 540 °C were obtained. Then X-ray diffraction (XRD), scanning electron microscopy (SEM) and X-ray energy dispersive spectrometer equipped (EDS) were used to analyze the phase information, microstructure and composition of the samples treated at the above temperatures. The results suggested that the oxidation and precipitation of Cu and Fe are the key reason to result in the failure of the material, which provides an important reference for the research of the magnet.
Temperature dependence of coercivity and magnetization reversal mechanism in Sm(Co/sub bal/Fe/sub 0.1/Cu/sub y/Zr/sub 0.04/)/sub 7.0/ magnets
The temperature dependence of coercivity in Sm(Co/sub bal/Fe/sub 0.1/Cu/sub y/Zr/sub 0.04/)/sub 7.0/ magnets with y=0 to 0.20 has been systematically studied. Two models are discussed with respect to the temperature dependence of coercivity. One model considers only domain wall (DW) pinning and explains the "abnormal" behavior as a temperature-induced transition from a repulsive DW pinning to an attractive one. The other model assumes a combination of pinning and nucleation of DWs in magnetically isolated 2:17 cells. In this model, the cell-boundary pinning of DWs takes place in the alloys with low Cu content at temperatures below the Curie temperature of 1:5 phase. Nucleation controls the magnetization reversal in the case of complete magnetic isolation of the 2:17 cells. This condition is expected to be fulfilled at any temperature above RT in the Cu-rich magnets, and only above Curie temperature of the 1:5 phase in the alloys with lower Cu content.
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.