Three-Dimensional Characterization of Superdislocation Interactions in the High Temperature and Low Stress Creep Regime of Ni-Base Superalloy Single Crystals
Monocrystaline Ni-base superalloys are the material of choice for first row blades in jet engine gas turbines. Using a novel visualization tool for 3D reconstruction and visualization of dislocation line segments from stereo-pairs of scanning transmission electron microscopies, the superdislocation substructures in Ni-base superalloy LEK 94 (crept to e = 26%) are characterized. Probable scenarios are discussed, how these dislocation substructures form.
On the nature of γ′ phase cutting and its effect on high temperature and low stress creep anisotropy of Ni-base single crystal superalloys
Abstract The creep anisotropy of the single crystal superalloy LEK 94 deformed in tension along [0 0 1] and [1 1 0] directions at 1293 K and 160 MPa was investigated. Elementary microstructural processes which are responsible for a higher increase in creep rates with strain during [1 1 0] as compared to [0 0 1] tensile loading were identified. [1 1 0] tensile creep is associated with a higher number of γ′ phase cutting events, where two dislocations with equal Burgers vectors of type 〈1 1 0〉 jointly shear the γ′ phase. The resulting 〈2 2 0〉-type superdislocation can move by glide. In contrast, during [0 0 1] tensile loading, two dislocations with different 〈1 1 0〉-type Burgers vectors must combine for γ′ phase cutting. The resulting 〈2 0 0〉-type superdislocations can only move by a combination of glide and climb. The evolution of dislocation networks during creep determines the nature of the γ′ phase cutting events. The higher [1 1 0] creep rates at strains exceeding 2% result from a combination of a higher number of cutting events (density of mobile dislocations in γ′) and a higher superdislocation mobility (〈2 2 0〉 glide) in the γ′ phase.
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.