Effects of annealing on phase evolution, microstructure and magnetic properties of mechanically synthesized nickel-ferrite
Abstract The influence of milling and subsequent annealing on nickel-ferrite phase formation was investigated by X-ray diffraction (XRD). Microstructure and magnetic properties of NiFe 2 O 4 were determined by field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM) and vibration sample magnetometry (VSM). Single phase nanosized nickel-ferrite was obtained by 30 h mechanical alloying (MA) and subsequent annealing at 600 °C for 1 h. Magnetic properties of the milled powder were extensively affected by the annealing temperature. Considerable growth of the particles and necking by sintering resulted from annealing at 1000 °C.
Effects of precursor types of Fe and Ni components on the properties of NiFe2O4 powders prepared by spray pyrolysis
Abstract The effects of the precursor types of Ni and Fe components on the morphology, mean size, and magnetic property of NiFe 2 O 4 powders prepared by spray pyrolysis from the spray solution, with citric acid were studied. The precursor powders with hollow and thin wall structure turned to the nano-sized NiFe 2 O 4 powders after post-treatment at a temperature of 800 °C. The nickel ferrite powders obtained from the spray solution with ferric chloride had nanometer sizes and narrow size distributions irrespective of the types of nickel precursor. The nickel ferrite powders obtained from the spray solution with ferric nitrate and nickel chloride also had nanometer size and narrow size distribution. The saturation magnetizations of the NiFe 2 O 4 powders changed from 37 to 42 emu/g according to the types of the Fe and Ni precursors. The saturation magnetizations of the NiFe 2 O 4 powders increased with increasing the Brunauer-Emmett-Teller (BET) surface areas of the powders.
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.