Carbides Evolution in a Ni-16Mo-7Cr Base Superalloy during Long-Term Thermal Exposure
The effect of long-term thermal exposure on the carbide evolution in a Ni-16Mo-7Cr base superalloy was investigated. The results show that M12C carbides are mainly precipitated on the grain boundaries during thermal exposure, and the primary massive M6C carbides can be completely transformed to M12C carbides in situ at temperatures above 750 °C for long-term thermal exposure. The transformation from M6C carbides to M12C carbides is attributed to the release of C atoms from M6C, which results in the morphology changes of massive carbides, and stabilization of the sizes of M12C carbides precipitated on the grain boundaries.
Evolution of carbide precipitates in Hastelloy N joints during welding and post weld heat treatment
Abstract The effects of welding and post weld heat treatment (PWHT) on the morphology, composition and lattice parameter of the precipitates in the welding joints of Hastelloy N alloy have been investigated. Joint samples were prepared using a gas tungsten arc welding process with ERNiMo-2 fillers, and then solid-solution treated at 1100 °C for 20 min. After welding, eutectic M6C carbides can be observed in the heat affected zones (HAZ) and weld zones (WZ). These eutectic M6C carbides become spheroidized during the following PWHT. The concentration of Si decreases during the transformation from the primary ones to eutectic ones, and then increases during the spheroidizing process. The total concentration of Si + Cr in M6C carbides remains stable during welding and PWHT. At the same time, the heat leads to the outward diffusion of Mo from the M6C carbides, which results in a decrease of the lattice parameter.
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.