Implementation of Gaussian Process Regression to strain data in residual stress measurements by hole drilling

Incremental hole drilling is a prominent tool in the surface and sub-surface residual stress measurements. Generally, strains are measured at drilling steps not corresponding to the stress calculation steps of the integral method. Consequently, strain measurements are fitted using polynomials or splines. However, this process is greatly influenced by user’s experience, affecting the calculated stresses as an additional source of uncertainty. The present work assesses the uncertainty associated with the strain fitting procedure by exploiting Gaussian Process Regression (GPR), a probabilistic machine learning framework which can yield uncertainties on the fit. These uncertainties were propagated to residual stresses by using Monte Carlo simulation. Laser shock peened AA 7050-T7451 specimens were considered as case study. The proposed methodology was corroborated by comparing residual stress results with those obtained exploiting traditional fitting procedures and X-ray diffraction measurements. Finally, the GPR-based approach demonstrated its ability to successfully discriminate different strain signal sources.