Evolution of a laser shock peened residual stress field locally with foreign object damage and subsequent fatigue crack growth. Acta Materialia, http://dx.doi.org/10.1016/j.actamat.2014.09.032 conditions. The ballistic FOD impacts were introduced by impacting a cube edge at an angle of head-on (0 degrees) to the leading edge. The residual stress distributions have been mapped by synchrotron X-ray diffraction prior to cracking and subsequent to short (~1 mm) and long (up to 6 mm) crack growth. 2The results suggest that the local residual stress field is highly stable even to the growth of relatively long cracks.

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a b s t r a c tForeign object damage (FOD) to the leading edge of aerofoils has been identified as one of the main life-limiting factors for aeroengine compressor blades. Laser-shock peening (LSP) has been proposed as a means of increasing the material's resistance to such impact damage. In this work, a three-dimensional finite element (FE) model has been developed to simulate the residual stresses due to head on (0°) and 45°impacts by a cuboidal projectile on aerofoil specimens treated with LSP. The Johnson-Cook (JC) material model was employed to describe the strain rate-dependent material behaviour; whilst the JohnsonCook dynamic failure model was considered in 45°FOD simulation, where significant loss of material occurred. The strain rate sensitivity of the model at selected high strain rates was assessed against the data from the literature. The numerical results from the simulation of head-on impact were compared with the measurements by depth-resolved synchrotron X-ray diffraction on the mid-plane. The models were then used to predict the 3D residual stress distributions due to 0°and 45°FOD impacts, and the results were compared with the strain maps obtained from high-energy synchrotron X-ray diffraction. Good to excellent correlations between the simulations and the measurements have been found.

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The current study investigates the effect of foreign object damage (FOD) on the pre-existing compressive residual stress field associated with laser shock peening (LSP) and its evolution upon combined LCF/HCF cycling. FOD was introduced onto an aerofoil-shaped specimen that had been previously LSP treated through ballistic impacts at angles of 0° and 45° to the leading edge. It is shown that the FOD notch created by 45° impact was asymmetric in shape and smaller in depth compared to that created at 0° impact. Significant through thickness compression was introduced parallel to the leading edge as a result of the LSP process. The residual strain distribution was mapped around the FOD notch by synchrotron X-ray radiation. The results show predominantly compressive stresses ahead of the notch, being greater for the 0 compared to 45 impact. No significant stress relaxation was observed after a combined (1000 HCF cycles superimposed on 1 LCF cycle) cycle.

Laser shock peening (LSP) as a surface treatment technique can improve the fatigue life and corrosion resistance of metallic materials by introducing significant compressive residual stresses near the surface. However, LSP-induced residual stresses are known to be dependent on a multitude of factors, such as laser process variables (spot size, pulse width and energy), component geometry, material properties and the peening sequence. In this study, an intelligent system based on machine learning was developed that can predict the residual stress distribution induced by LSP. The system can also be applied to “reverse-optimise” the process parameters. The prediction system was developed using residual stress data derived from incremental hole drilling. We used artificial neural networks (ANNs) within a Bayesian framework to develop a robust prediction model validated using a comprehensive set of case studies. We also studied the relative importance of the LSP process parameters using Garson’s algorithm and parametric studies to understand the response of the residual stresses in laser peening systems as a function of different process variables. Furthermore, this study critically evaluates the developed machine learning models while demonstrating the potential benefits of implementing an intelligent system in prediction and optimisation strategies of the laser shock peening process.