Heterogeneous nucleation of β-type precipitates on nanoscale Zr-rich particles in a Mg-6Zn-0.5Cu-0.6Zr alloy
Zirconium (Zr) is an important alloying element to Mg-Zn-based alloy system. In this paper, we report the formation of the β-type precipitates on the nanoscale Zr-rich particles in a Mg-6Zn-0.5Cu-0.6Zr alloy during ageing at 180°C. Scanning transmission electron microscopy examinations revealed that the nanoscale Zr-rich [0001]α rods/laths are dominant in the Zr-rich core regions of the as-quenched sample after a solution treatment at 430°C. More significantly, these Zr-rich particles served as favourable sites for heterogeneous nucleation of the Zn-rich β-type phase during subsequent isothermal ageing at 180°C. This research provides a potential route to engineer precipitate microstructure for better strengthening effect in the Zr-containing Mg alloys.
Hydrogen-induced decomposition of Zr-rich cores in an Mg−6Zn−0.6Zr−0.5Cu alloy
Abstract This study presents the first experimental evidence of a hydrogen-induced decomposition reaction in an Mg–6Zn–0.6Zr–0.5Cu alloy from combined transmission electron microscopy and atom-probe tomography characterization. The reaction takes place due to the presence of H in the Mg matrix, causes the decomposition of pre-existing, high-temperature Zr–Zn intermetallic rods into Zr-rich hydride and β ′ (Zn 3 Mg 2 ), and forms novel composite precipitates in the Zr-rich cores of the alloy during ageing at 180 °C. The stoichiometry of the Zr–Zn rods was found to be Zn 3 (Zr 1− x , Mg x ) 2 , rather than Zn 2 Zr 3 , although both have a similar tetragonal crystal structure. The intrinsic link between the high-temperature Zr–Zn rods and the subsequent elongated composite precipitates, as depicted by the reaction, highlights the importance of engineering the Zr–Zn rod microstructures to control the final precipitate microstructure and effectively strengthen the Zr-rich cores, and hence the advanced Mg alloys.
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.