TEM study of radiation induced defects in baffle-former-barrel assembly from decommissioned NPP Greifswald
A complex transmission electron microscopy (TEM) study of reactor vessel internal (RVI) materials from the baffle-former-barrel assembly from NPP Greifswald (VVER 440), Unit 1 decommissioned after 15 service cycles has been undertaken. All parts of the baffle-former-barrel assembly are made from Ti-stabilized austenitic stainless steel 08Ch18N10T. The materials were exposed to different dose of neutron radiation (2.4 - 11.4 dpa) at temperatures 267 - 398 °C depending on position in the core. Three types of radiation induced defects were identified and quantified, namely: dislocations, cavities (voids) and fine-scaled precipitated particles of Ni-Si rich phases. Black-dot type defects were observed too. Operation conditions are around ≈ 300 °C that is why we have observed defect typical for both low and high regions of irradiation temperatures.
Evolution of fine-scale defects in stainless steels neutron-irradiated at 275 C
Six austenitic stainless steel heats (three heats each of 304SS and 316SS) neutron-irradiated at 275 °C from 0.6 to 13.3 dpa have been carefully characterized by TEM and their hardness measured as a function of dose. The characterization revealed that the microstructure is dominated by a very high density of small Frank loops present in sizes as small as 1 nm and perhaps lower, which could be of both vacancy and interstitial-type. Frank loop density saturated at the lowest doses characterized, whereas the Frank loop size distributions changed with increasing dose from an initially narrow, symmetric shape to a broader, asymmetric shape. Although substantial hardening is caused by the small defects, a simple correlation between hardness changes and density and size of defects does not exist. These results indicate that radiation-induced segregation to the Frank loops could play a role in both defect evolution and hardening response.
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.