A Hybrid Measurement-Inferencing Method for Modeling Residual Stress Field Based on Heteroscedastic Latent Gaussian Process

The residual stress inside a component is one of the main factors affecting its material property and manufacturing quality, such as geometric stability and fatigue life. It is important to understand the characteristics of the residual stress across the volume of a component, referred to as its residual stress field. However, existing destructive and non-destructive methods cannot accurately and efficiently measure/model the residual stress across the whole component material due to insufficient measurement data. This paper presents a novel method for modelling the residual stress field by fusing both local and global measurement data. It is based on the heteroscedastic latent Gaussian process (HLGP), where different measurement variances and the heteroscedasticity of the residual stress field can be addressed. A residual stress field can be modelled as a latent Gaussian process along the global material space, and its different means and variances are controlled by two latent Gaussian processes. This method takes the advantages of existing measurement and inferencing methods to model the residual stress field more accurately and reliably, which was approved by both simulation and actual experimental results on typical structural components, compared with the latent Gaussian process and homoscedastic latent Gaussian process currently used for modelling the residual stress field.