Högskolan i Skövde

Residual stress (RS) within titanium alloy structural components is the primary factor contributing to machining deformation. It comprises initial residual stress （IRS） and machined surface residual stress (MSRS), resulting from the interplay between IRS and high-level machining-induced residual stress MIRS). Machining deformation of components poses a significant challenge in the aerospace industry，and accurately assessing RS is crucial for precise prediction and control. However, current RS prediction methods struggle to account for various uncertainties in the component manufacturing process，leading to limited prediction accuracy. Furthermore, existing measurement methods can only gauge local RS in samples，which proves inefficient and unreliable for measuring RS fields in large components. Addressing these challenges, this paper introduces a method for simultaneously estimating IRS and MSRS within titanium alloy aircraft components using a Bayesian framework. This approach treats IRS and MSRS as unobservable fields modeled by Gaussian processes. It leverages observable deformation force data to estimate IRS and MSRS while incorporating prior correlations between MSRS fields. In this context，the prior correlation between MSRS fields is represented as a latent Gaussian process with a shared covariance function. The proposed method offers an effective means of estimating the RS field using deformation force data from a probabilistic perspective. It serves as a dependable foundation for optimizing subsequent deformation control strategies.