Dislocation Loop Formation and Growth under In Situ Laser and/or Electron Irradiation
Vacancies and interstitial atoms are primary lattice (point) defects that cause observable microstructural changes, such as the formation of dislocation loops and voids in crystalline solids. These defects' diffusion properties determine the phase stability and environmental resistibility of macroscopic materials under ambient conditions. Although in situ methods have been proposed for measuring the diffusion energy of point defects, direct measurement has been limited. In this study, we propose an alternative in situ method to measure the activation energy for vacancy migration under laser irradiation using a pulsed laser beam from a laser-equipped high-voltage electron microscope (laser-HVEM). We made in situ observations that revealed the formation and growth of vacancy dislocation loops in an austenitic stainless steel during laser irradiation. These loops continued to grow when thermal annealing was performed after laser irradiation at the same temperature. We anticipate that laser-HVEM will provide a new method for investigating lattice defects.
One-Dimensional Fast Migration of Vacancy Clusters in Metals
The migration of point defects, for example, crystal lattice vacancies and self-interstitial atoms (SIAs), typically occurs through three-dimensional random walk in crystalline solids. However, when vacancies and SIAs agglomerate to form planar clusters, the migration mode may change. We observed nanometer-sized clusters of vacancies exhibiting one-dimensional (1D) fast migration. The 1D migration transported a vacancy cluster containing several hundred vacancies with a mobility higher than that of a single vacancy random walk and a mobility comparable to a single SIA random walk. Moreover, we found that the 1D migration may be a key physical mechanism for self-organization of nanometer-sized sessile vacancy cluster (stacking fault tetrahedron) arrays. Harnessing this 1D migration mode may enable new control of defect microstructures such as effective defect removal and introduction of ordered nanostructures in materials, including semiconductors.
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.