Effect of broaching on high-temperature fatigue behavior in notched specimens of INCONEL 718
Notches were machined in specimens of INCONEL 718 by a broaching process, where differing broaching runs led to differing extents of subsurface deformation and surface roughness. Fatigue tests were carried out at 600 °C with a trapezoidal loading waveform at 0.25 Hz. The broaching process that led to the more severe subsurface deformation (but lower surface roughness) showed the worst fatigue performance. Analysis of total strain amplitude in the notch root with the aid of an elastoplastic finite-element (FE) model showed that the work hardening related to the subsurface deformation caused by the different broaching can account for the difference in fatigue lives. Differences in initiation and growth behavior were seen for the two broached finishes as well as for broached and subsequently polished samples. These differences are discussed in terms of a change in crack growth initiation and growth mechanisms due to the presence of the work-hardened layer.
Surface Effects on Low Cycle Fatigue Behavior in IN718 Alloy
Superalloys used in gas turbine engine rotating components require superior low cycle fatigue (LCF) properties, but often, the intrinsic LCF capability of the rotor, as determined by laboratory specimen tests, is limited by presence of fine particles such as carbides, borides and ceramic inclusions, as well as, pores, voids, and large grains in the alloy. Further, manufacturing processes such as turning, milling, and broaching can limit LCF life if they introduce surface damage and/or tensile residual stress. In IN718, prior studies have shown that fatigue initiation from MC carbides is promoted at lower test temperature and high stress, and the LCF life is significantly reduced. The present study investigates the effect of machining that can introduce carbide damage on LCF life, and a few post-machine processing methods for LCF recovery. The IN718 material used in this study came from a gas turbine disk forging that was directly aged without the conventional solution heat-treatment. The microstructure was fine-grained with an average grain size of 11 μm. Smooth and notched LCF properties were evaluated with machined and polished surfaces at test temperature and stress that promoted carbide initiation of fatigue cracks. Majority of the tests were performed at temperature of 560 K and at stresses of 1102 and 1240 MPa for smooth, and nominal stresses of 595 and 650 MPa for notched specimens. The min to max stress ratio (R) was 0.05. The cycles to failure were analyzed in terms of machining effect on surface carbides, and the fatigue initiation sites were studied by scanning electron microscopy (SEM). Smooth LCF specimens included asmachined (lathe-turned) and polished (600 grit SiC) surfaces and the notched LCF specimens included single-point bored (SPB) and media-polished notch surfaces. Lathe-turning and SPB cracked or damaged surface and subsurface carbides, considerably impacting the LCF life by factors of 3-15X (depending on test conditions). For a smooth lathe-turned surface, shot peening was effective in recovering LCF life by 5-10X at minimum over the as-machined surface. Some conventional post-machine processes for holes, such as mechanical honing, extrude hone and jig grinding and their influence on LCF life were studied as well.
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.