Full-field analysis of damage under complex thermomechanical loading

This study combines infrared thermography, digital image correlation and machine learning to measure respec-tively temperature, strain and damage fields at high temperature. This is applied to thermomechanical fatigue (TMF) testing in presence of severe gradient for typical out-of-phase loading condition. This type of loading is challenging when dealing with thin sheets due to buckling risk induced by high temperature compression. For a Co-based superalloy (Haynes 188), this study demonstrates full-field identification/validation of both behaviour and damage models. Thermal gradient model is the key point in this analysis. With the coupling of TMF measurements by machine learning and DIC, local fatigue micro-crack growth rate and localisation are assessed through jump in displacement. TMF finite element analysis, of loading and damage, validates the whole model framework.

Automated summary

This study combines infrared thermography, digital image correlation, and machine learning to measure temperature, strain, and damage fields at high temperatures. It is applied to thermomechanical fatigue (TMF) testing with severe gradients for typical out-of-phase loading conditions. The study demonstrates the identification/validation of behavior and damage models for a Co-based superalloy (Haynes 188). The key point in this analysis is the thermal gradient model. TMF finite element analysis validates the entire model framework for loading and damage.