The surface integrity of a machined surface is a critical requirement in high performance applications such as gas turbine foils. Multiple aspects are incorporated in surface integrity, such as surface finish, changes in microstructure, and consequently residual stresses. This research focuses on detection of the presence of the subsurface damage and estimation of the depth of the damage, as related to the changes in microstructure. A statistically-based methodology for estimation of the depth of the machining affected zone (MAZ) using scanning electron microscope (SEM) images is developed and incorporated in MATLAB software, with the objective of having an automated and also more exact method than optical evaluation. The method is applied for estimation of the depth of the MAZ for nickel-based superalloys. The results of this work are being extended to correlate the MAZ with cutting parameters and conditions to determine the onset of subsurface damage, predict the extent of the MAZ and, ultimately, control its characteristics. Furthermore, this method can be extended for the analysis of different aspects of the microstructure (i.e., dimensions of grains) and estimating the amount of strain and residual stress in the damaged layer.

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